ML-For-Beginners/7-TimeSeries/2-ARIMA/solution/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series forecasting with ARIMA\n",
    "\n",
    "In this notebook, we demonstrate how to:\n",
    "- prepare time series data for training an ARIMA time series forecasting model\n",
    "- implement a simple ARIMA model to forecast the next HORIZON steps ahead (time *t+1* through *t+HORIZON*) in the time series\n",
    "- evaluate the model \n",
    "\n",
    "\n",
    "The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n",
    "\n",
    "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in /Users/alfredo/miniforge3/envs/tmp/lib/python3.8/site-packages (0.12.2)\n",
      "Requirement already satisfied: numpy>=1.15 in /Users/alfredo/miniforge3/envs/tmp/lib/python3.8/site-packages (from statsmodels) (1.21.1)\n",
      "Requirement already satisfied: pandas>=0.21 in /Users/alfredo/miniforge3/envs/tmp/lib/python3.8/site-packages (from statsmodels) (1.3.1)\n",
      "Requirement already satisfied: scipy>=1.1 in /Users/alfredo/miniforge3/envs/tmp/lib/python3.8/site-packages (from statsmodels) (1.7.0)\n",
      "Requirement already satisfied: patsy>=0.5 in /Users/alfredo/miniforge3/envs/tmp/lib/python3.8/site-packages (from statsmodels) (0.5.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/alfredo/miniforge3/envs/tmp/lib/python3.8/site-packages (from pandas>=0.21->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Users/alfredo/miniforge3/envs/tmp/lib/python3.8/site-packages (from pandas>=0.21->statsmodels) (2021.1)\n",
      "Requirement already satisfied: six in /Users/alfredo/miniforge3/envs/tmp/lib/python3.8/site-packages (from patsy>=0.5->statsmodels) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import math\n",
    "\n",
    "from pandas.plotting import autocorrelation_plot\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from common.utils import load_data, mape\n",
    "from IPython.display import Image\n",
    "\n",
    "%matplotlib inline\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2,698.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>2,453.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>2,560.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>2,719.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>2,916.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>3,105.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2012-01-01 00:00:00 2,698.00\n",
       "2012-01-01 01:00:00 2,558.00\n",
       "2012-01-01 02:00:00 2,444.00\n",
       "2012-01-01 03:00:00 2,402.00\n",
       "2012-01-01 04:00:00 2,403.00\n",
       "2012-01-01 05:00:00 2,453.00\n",
       "2012-01-01 06:00:00 2,560.00\n",
       "2012-01-01 07:00:00 2,719.00\n",
       "2012-01-01 08:00:00 2,916.00\n",
       "2012-01-01 09:00:00 3,105.00"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy = load_data('./data')[['load']]\n",
    "energy.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot all available load data (January 2012 to Dec 2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create training and testing data sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_start_dt = '2014-11-01 00:00:00'\n",
    "test_start_dt = '2014-12-30 00:00:00'    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (1416, 1)\n",
      "Test data shape:  (48, 1)\n"
     ]
    }
   ],
   "source": [
    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
    "\n",
    "print('Training data shape: ', train.shape)\n",
    "print('Test data shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 05:00:00</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 06:00:00</th>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 07:00:00</th>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 08:00:00</th>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 09:00:00</th>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-11-01 00:00:00  0.10\n",
       "2014-11-01 01:00:00  0.07\n",
       "2014-11-01 02:00:00  0.05\n",
       "2014-11-01 03:00:00  0.04\n",
       "2014-11-01 04:00:00  0.06\n",
       "2014-11-01 05:00:00  0.10\n",
       "2014-11-01 06:00:00  0.19\n",
       "2014-11-01 07:00:00  0.31\n",
       "2014-11-01 08:00:00  0.40\n",
       "2014-11-01 09:00:00  0.48"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train['load'] = scaler.fit_transform(train)\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original vs scaled data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also scale the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-30 00:00:00</th>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 01:00:00</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 02:00:00</th>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 03:00:00</th>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 04:00:00</th>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-12-30 00:00:00  0.33\n",
       "2014-12-30 01:00:00  0.29\n",
       "2014-12-30 02:00:00  0.27\n",
       "2014-12-30 03:00:00  0.27\n",
       "2014-12-30 04:00:00  0.30"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['load'] = scaler.transform(test)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement ARIMA method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecasting horizon: 3 hours\n"
     ]
    }
   ],
   "source": [
    "# Specify the number of steps to forecast ahead\n",
    "HORIZON = 3\n",
    "print('Forecasting horizon:', HORIZON, 'hours')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.45318D+00    |proj g|=  2.50788D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.45390D+00    |proj g|=  4.50193D-01\n",
      "\n",
      "At iterate   10    f= -2.45541D+00    |proj g|=  1.49646D+00\n",
      "\n",
      "At iterate   15    f= -2.45565D+00    |proj g|=  5.89642D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     19     27      1     0     0   2.844D-02  -2.456D+00\n",
      "  F =  -2.4556536226532630     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "                                     SARIMAX Results                                      \n",
      "==========================================================================================\n",
      "Dep. Variable:                               load   No. Observations:                 1416\n",
      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.206\n",
      "Date:                            Mon, 09 Aug 2021   AIC                          -6942.411\n",
      "Time:                                    16:51:09   BIC                          -6910.984\n",
      "Sample:                                11-01-2014   HQIC                         -6930.659\n",
      "                                     - 12-29-2014                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.8402      0.016     52.430      0.000       0.809       0.872\n",
      "ar.L2         -0.5189      0.034    -15.331      0.000      -0.585      -0.453\n",
      "ar.L3          0.1537      0.044      3.461      0.001       0.067       0.241\n",
      "ar.L4         -0.0767      0.036     -2.121      0.034      -0.148      -0.006\n",
      "ar.S.L24      -0.2373      0.024     -9.953      0.000      -0.284      -0.191\n",
      "sigma2         0.0004   8.32e-06     47.340      0.000       0.000       0.000\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.04   Jarque-Bera (JB):              1469.19\n",
      "Prob(Q):                              0.85   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.84   Skew:                             0.13\n",
      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.03\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "order = (4, 1, 0)\n",
    "seasonal_order = (1, 1, 0, 24)\n",
    "\n",
    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
    "results = model.fit()\n",
    "\n",
    "print(results.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a test data point for each HORIZON step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>load+1</th>\n",
       "      <th>load+2</th>\n",
       "      <th>load+3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-30 00:00:00</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 01:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 02:00:00</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 03:00:00</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 04:00:00</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load  load+1  load+2  load+3\n",
       "2014-12-30 00:00:00  0.33    0.29    0.27    0.27\n",
       "2014-12-30 01:00:00  0.29    0.27    0.27    0.30\n",
       "2014-12-30 02:00:00  0.27    0.27    0.30    0.41\n",
       "2014-12-30 03:00:00  0.27    0.30    0.41    0.57\n",
       "2014-12-30 04:00:00  0.30    0.41    0.57    0.68"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_shifted = test.copy()\n",
    "\n",
    "for t in range(1, HORIZON+1):\n",
    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
    "    \n",
    "test_shifted = test_shifted.dropna(how='any')\n",
    "test_shifted.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make predictions on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.41942D+00    |proj g|=  1.50341D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.41971D+00    |proj g|=  5.00564D-01\n",
      "\n",
      "At iterate   10    f= -2.42249D+00    |proj g|=  2.36045D-01\n",
      "\n",
      "At iterate   15    f= -2.42255D+00    |proj g|=  2.72197D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   20    f= -2.42282D+00    |proj g|=  4.05307D-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  ys=-1.714E-08  -gs= 3.691E-08 BFGS update SKIPPED\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     22    100      3     1     0   1.363D-02  -2.423D+00\n",
      "  F =  -2.4228185698999325     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 00:00:00\n",
      "1 : predicted = [0.32 0.29 0.28 0.29] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.41939D+00    |proj g|=  1.51571D+00\n",
      "\n",
      "At iterate    5    f= -2.41975D+00    |proj g|=  3.05038D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   10    f= -2.42262D+00    |proj g|=  4.03318D-02\n",
      "\n",
      "At iterate   15    f= -2.42262D+00    |proj g|=  3.31965D-02\n",
      "\n",
      "At iterate   20    f= -2.42262D+00    |proj g|=  9.67282D-03\n",
      "\n",
      "At iterate   25    f= -2.42278D+00    |proj g|=  8.23940D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   30    f= -2.42278D+00    |proj g|=  5.32895D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     30     84      2     0     0   5.329D-03  -2.423D+00\n",
      "  F =  -2.4227773617326398     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 01:00:00\n",
      "2 : predicted = [0.3  0.29 0.3  0.34] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.42057D+00    |proj g|=  1.40865D+00\n",
      "\n",
      "At iterate    5    f= -2.42091D+00    |proj g|=  9.39400D-01\n",
      "\n",
      "At iterate   10    f= -2.42373D+00    |proj g|=  1.14278D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -2.42388D+00    |proj g|=  6.86263D-03\n",
      "  ys=-2.122E-12  -gs= 1.158E-11 BFGS update SKIPPED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     17     98      3     1     0   7.145D-03  -2.424D+00\n",
      "  F =  -2.4238797046085039     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 02:00:00\n",
      "3 : predicted = [0.27 0.28 0.32 0.42] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.42198D+00    |proj g|=  8.36871D-01\n",
      "\n",
      "At iterate    5    f= -2.42207D+00    |proj g|=  2.86964D-01\n",
      "\n",
      "At iterate   10    f= -2.42384D+00    |proj g|=  4.70590D-02\n",
      "\n",
      "At iterate   15    f= -2.42528D+00    |proj g|=  7.61228D-02\n",
      "\n",
      "At iterate   20    f= -2.42539D+00    |proj g|=  8.20616D-02\n",
      "\n",
      "At iterate   25    f= -2.42539D+00    |proj g|=  8.08397D-02\n",
      "\n",
      "At iterate   30    f= -2.42553D+00    |proj g|=  7.64819D-02\n",
      "\n",
      "At iterate   35    f= -2.42553D+00    |proj g|=  1.04767D-01\n",
      "\n",
      "At iterate   40    f= -2.42554D+00    |proj g|=  8.73757D-02\n",
      "\n",
      "At iterate   45    f= -2.42555D+00    |proj g|=  1.68714D-02\n",
      "\n",
      "At iterate   50    f= -2.42555D+00    |proj g|=  2.05593D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     50     97      1     0     0   2.056D-02  -2.426D+00\n",
      "  F =  -2.4255493534965393     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "2014-12-30 03:00:00\n",
      "4 : predicted = [0.28 0.32 0.42 0.57] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.42388D+00    |proj g|=  1.06644D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.42761D+00    |proj g|=  1.28108D+00\n",
      "\n",
      "At iterate   10    f= -2.42813D+00    |proj g|=  1.14965D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     13     26      1     0     0   6.507D-02  -2.428D+00\n",
      "  F =  -2.4281299217408261     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 04:00:00\n",
      "5 : predicted = [0.3  0.39 0.54 0.65] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.42722D+00    |proj g|=  2.23014D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.42773D+00    |proj g|=  6.70957D-01\n",
      "\n",
      "At iterate   10    f= -2.43174D+00    |proj g|=  9.71137D-01\n",
      "\n",
      "At iterate   15    f= -2.43184D+00    |proj g|=  1.34708D-02\n",
      "\n",
      "At iterate   20    f= -2.43202D+00    |proj g|=  2.44061D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     25     81      2     0     0   1.346D-02  -2.432D+00\n",
      "  F =  -2.4320359948721992     \n",
      "\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH                              \n",
      "2014-12-30 05:00:00\n",
      "6 : predicted = [0.4  0.55 0.66 0.74] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Line search cannot locate an adequate point after MAXLS\n",
      "  function and gradient evaluations.\n",
      "  Previous x, f and g restored.\n",
      " Possible causes: 1 error in function or gradient evaluation;\n",
      "                  2 rounding error dominate computation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.45302D+00    |proj g|=  2.36751D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.45313D+00    |proj g|=  8.76462D-01\n",
      "\n",
      "At iterate   10    f= -2.45592D+00    |proj g|=  4.47503D-01\n",
      "\n",
      "At iterate   15    f= -2.45594D+00    |proj g|=  1.26598D-02\n",
      "\n",
      "At iterate   20    f= -2.45596D+00    |proj g|=  1.20783D-02\n",
      "\n",
      "At iterate   25    f= -2.45604D+00    |proj g|=  5.08466D-01\n",
      "\n",
      "At iterate   30    f= -2.45607D+00    |proj g|=  1.41293D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     31     40      1     0     0   1.431D-02  -2.456D+00\n",
      "  F =  -2.4560651421758215     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 06:00:00\n",
      "7 : predicted = [0.57 0.68 0.75 0.8 ] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.45817D+00    |proj g|=  8.10715D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.45832D+00    |proj g|=  7.54452D-01\n",
      "\n",
      "At iterate   10    f= -2.46104D+00    |proj g|=  1.13770D+00\n",
      "\n",
      "At iterate   15    f= -2.46116D+00    |proj g|=  6.05033D-02\n",
      "\n",
      "At iterate   20    f= -2.46118D+00    |proj g|=  2.83252D-02\n",
      "\n",
      "At iterate   25    f= -2.46125D+00    |proj g|=  2.57130D-01\n",
      "\n",
      "At iterate   30    f= -2.46128D+00    |proj g|=  1.05648D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     30     51      1     0     0   1.056D-03  -2.461D+00\n",
      "  F =  -2.4612773455222370     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 07:00:00\n",
      "8 : predicted = [0.68 0.75 0.8  0.82] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.46194D+00    |proj g|=  1.17532D+00\n",
      "\n",
      "At iterate    5    f= -2.46209D+00    |proj g|=  4.40744D-01\n",
      "\n",
      "At iterate   10    f= -2.46398D+00    |proj g|=  2.41425D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     13     19      1     0     0   1.775D-02  -2.465D+00\n",
      "  F =  -2.4646583010793863     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 08:00:00\n",
      "9 : predicted = [0.75 0.8  0.82 0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47439D+00    |proj g|=  2.05841D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47481D+00    |proj g|=  3.68345D-01\n",
      "\n",
      "At iterate   10    f= -2.47633D+00    |proj g|=  2.26482D+00\n",
      "\n",
      "At iterate   15    f= -2.47689D+00    |proj g|=  1.02275D-02\n",
      "\n",
      "At iterate   20    f= -2.47695D+00    |proj g|=  1.88640D-02\n",
      "\n",
      "At iterate   25    f= -2.47697D+00    |proj g|=  4.38279D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     28     45      1     0     0   8.354D-03  -2.477D+00\n",
      "  F =  -2.4769653304940942     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 09:00:00\n",
      "10 : predicted = [0.76 0.78 0.78 0.77] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47407D+00    |proj g|=  1.97382D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47446D+00    |proj g|=  3.70272D-01\n",
      "\n",
      "At iterate   10    f= -2.47666D+00    |proj g|=  3.68982D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     12     32      1     0     0   3.025D-02  -2.477D+00\n",
      "  F =  -2.4766790705909720     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 10:00:00\n",
      "11 : predicted = [0.76 0.75 0.74 0.73] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47425D+00    |proj g|=  1.98710D+00\n",
      "\n",
      "At iterate    5    f= -2.47465D+00    |proj g|=  4.50665D-01\n",
      "\n",
      "At iterate   10    f= -2.47687D+00    |proj g|=  5.88934D-01\n",
      "\n",
      "At iterate   15    f= -2.47691D+00    |proj g|=  1.88765D-02\n",
      "\n",
      "At iterate   20    f= -2.47695D+00    |proj g|=  1.25762D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     22     37      1     0     0   2.326D-02  -2.477D+00\n",
      "  F =  -2.4769525238258274     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 11:00:00\n",
      "12 : predicted = [0.77 0.76 0.75 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47264D+00    |proj g|=  1.96959D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47302D+00    |proj g|=  3.73010D-01\n",
      "\n",
      "At iterate   10    f= -2.47526D+00    |proj g|=  2.24601D-01\n",
      "  ys=-2.607E-11  -gs= 4.134E-11 BFGS update SKIPPED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -2.47527D+00    |proj g|=  3.04083D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     15     42      1     1     0   3.041D-02  -2.475D+00\n",
      "  F =  -2.4752715914226688     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 12:00:00\n",
      "13 : predicted = [0.7  0.68 0.69 0.72] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47219D+00    |proj g|=  2.02081D+00\n",
      "\n",
      "At iterate    5    f= -2.47260D+00    |proj g|=  4.11979D-01\n",
      "\n",
      "At iterate   10    f= -2.47470D+00    |proj g|=  1.86047D+00\n",
      "\n",
      "At iterate   15    f= -2.47502D+00    |proj g|=  1.52350D-01\n",
      "\n",
      "At iterate   20    f= -2.47512D+00    |proj g|=  4.66093D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     21     62      2     0     0   3.281D-02  -2.475D+00\n",
      "  F =  -2.4751155788669172     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 13:00:00\n",
      "14 : predicted = [0.72 0.73 0.76 0.88] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47247D+00    |proj g|=  2.06965D+00\n",
      "\n",
      "At iterate    5    f= -2.47290D+00    |proj g|=  4.16108D-01\n",
      "\n",
      "At iterate   10    f= -2.47463D+00    |proj g|=  2.51081D+00\n",
      "\n",
      "At iterate   15    f= -2.47534D+00    |proj g|=  2.14054D-02\n",
      "\n",
      "At iterate   20    f= -2.47537D+00    |proj g|=  2.46302D-01\n",
      "\n",
      "At iterate   25    f= -2.47542D+00    |proj g|=  2.46054D-01\n",
      "\n",
      "At iterate   30    f= -2.47542D+00    |proj g|=  5.41548D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     30     45      1     0     0   5.415D-03  -2.475D+00\n",
      "  F =  -2.4754246647110363     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 14:00:00\n",
      "15 : predicted = [0.71 0.73 0.86 0.97] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47262D+00    |proj g|=  1.65968D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47305D+00    |proj g|=  1.14681D+00\n",
      "\n",
      "At iterate   10    f= -2.47548D+00    |proj g|=  1.63458D-01\n",
      "\n",
      "At iterate   15    f= -2.47551D+00    |proj g|=  1.54294D-02\n",
      "\n",
      "At iterate   20    f= -2.47554D+00    |proj g|=  8.51589D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     24     68      2     0     0   1.495D-03  -2.476D+00\n",
      "  F =  -2.4755571790928355     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 15:00:00\n",
      "16 : predicted = [0.73 0.85 0.97 0.95] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47454D+00    |proj g|=  1.83674D+00\n",
      "\n",
      "At iterate    5    f= -2.47488D+00    |proj g|=  4.62224D-01\n",
      "\n",
      "At iterate   10    f= -2.47723D+00    |proj g|=  2.19051D-02\n",
      "\n",
      "At iterate   15    f= -2.47725D+00    |proj g|=  4.16635D-01\n",
      "\n",
      "At iterate   20    f= -2.47731D+00    |proj g|=  2.71948D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     22     39      1     0     0   9.412D-03  -2.477D+00\n",
      "  F =  -2.4773121280598058     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 16:00:00\n",
      "17 : predicted = [0.87 0.99 0.97 0.91] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47496D+00    |proj g|=  1.95433D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47534D+00    |proj g|=  4.67116D-01\n",
      "\n",
      "At iterate   10    f= -2.47723D+00    |proj g|=  2.00118D+00\n",
      "\n",
      "At iterate   15    f= -2.47765D+00    |proj g|=  1.32823D-02\n",
      "\n",
      "At iterate   20    f= -2.47769D+00    |proj g|=  1.61726D-01\n",
      "\n",
      "At iterate   25    f= -2.47773D+00    |proj g|=  1.63493D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     29     89      2     0     0   1.787D-02  -2.478D+00\n",
      "  F =  -2.4777328090947539     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 17:00:00\n",
      "18 : predicted = [0.94 0.92 0.86 0.8 ] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47581D+00    |proj g|=  1.98036D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47620D+00    |proj g|=  5.08252D-01\n",
      "\n",
      "At iterate   10    f= -2.47813D+00    |proj g|=  1.42282D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      "\n",
      " Line search cannot locate an adequate point after MAXLS\n",
      "  function and gradient evaluations.\n",
      "  Previous x, f and g restored.\n",
      " Possible causes: 1 error in function or gradient evaluation;\n",
      "                  2 rounding error dominate computation.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     15     59      2     0     0   3.443D-02  -2.478D+00\n",
      "  F =  -2.4783155403487278     \n",
      "\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH                              \n",
      "2014-12-30 18:00:00\n",
      "19 : predicted = [0.94 0.89 0.82 0.71] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47585D+00    |proj g|=  1.98205D+00\n",
      "\n",
      "At iterate    5    f= -2.47624D+00    |proj g|=  4.07768D-01\n",
      "\n",
      "At iterate   10    f= -2.47787D+00    |proj g|=  2.15931D+00\n",
      "\n",
      "At iterate   15    f= -2.47836D+00    |proj g|=  4.27968D-02\n",
      "\n",
      "At iterate   20    f= -2.47842D+00    |proj g|=  2.93748D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     22     65      2     0     0   1.279D-02  -2.478D+00\n",
      "  F =  -2.4784305928324923     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 19:00:00\n",
      "20 : predicted = [0.88 0.82 0.71 0.57] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47584D+00    |proj g|=  1.98236D+00\n",
      "\n",
      "At iterate    5    f= -2.47623D+00    |proj g|=  4.02650D-01\n",
      "\n",
      "At iterate   10    f= -2.47793D+00    |proj g|=  2.01582D+00\n",
      "\n",
      "At iterate   15    f= -2.47835D+00    |proj g|=  1.04204D-02\n",
      "\n",
      "At iterate   20    f= -2.47842D+00    |proj g|=  4.81576D-02\n",
      "\n",
      "At iterate   25    f= -2.47842D+00    |proj g|=  3.03180D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     27     52      1     0     0   3.309D-02  -2.478D+00\n",
      "  F =  -2.4784198183110195     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 20:00:00\n",
      "21 : predicted = [0.83 0.72 0.58 0.47] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47613D+00    |proj g|=  2.01164D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47668D+00    |proj g|=  7.22532D-01\n",
      "\n",
      "At iterate   10    f= -2.47867D+00    |proj g|=  1.76459D-02\n",
      "\n",
      "At iterate   15    f= -2.47868D+00    |proj g|=  3.27991D-02\n",
      "\n",
      "At iterate   20    f= -2.47870D+00    |proj g|=  1.31518D-01\n",
      "\n",
      "At iterate   25    f= -2.47874D+00    |proj g|=  3.56529D-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      "\n",
      " Line search cannot locate an adequate point after MAXLS\n",
      "  function and gradient evaluations.\n",
      "  Previous x, f and g restored.\n",
      " Possible causes: 1 error in function or gradient evaluation;\n",
      "                  2 rounding error dominate computation.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     26     81      2     0     0   3.565D-03  -2.479D+00\n",
      "  F =  -2.4787420745788977     \n",
      "\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH                              \n",
      "2014-12-30 21:00:00\n",
      "22 : predicted = [0.72 0.58 0.47 0.39] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47619D+00    |proj g|=  2.01926D+00\n",
      "\n",
      "At iterate    5    f= -2.47660D+00    |proj g|=  4.43957D-01\n",
      "\n",
      "At iterate   10    f= -2.47869D+00    |proj g|=  1.69971D-01\n",
      "\n",
      "At iterate   15    f= -2.47881D+00    |proj g|=  3.51451D-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     18     39      1     0     0   4.418D-03  -2.479D+00\n",
      "  F =  -2.4788136349332017     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 22:00:00\n",
      "23 : predicted = [0.58 0.47 0.39 0.35] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47623D+00    |proj g|=  2.02380D+00\n",
      "\n",
      "At iterate    5    f= -2.47663D+00    |proj g|=  4.05651D-01\n",
      "\n",
      "At iterate   10    f= -2.47823D+00    |proj g|=  2.26486D+00\n",
      "\n",
      "At iterate   15    f= -2.47879D+00    |proj g|=  9.98917D-02\n",
      "\n",
      "At iterate   20    f= -2.47886D+00    |proj g|=  8.64486D-02\n",
      "\n",
      "At iterate   25    f= -2.47887D+00    |proj g|=  1.93558D-02\n",
      "\n",
      "At iterate   30    f= -2.47887D+00    |proj g|=  7.08864D-02\n",
      "\n",
      "At iterate   35    f= -2.47887D+00    |proj g|=  1.48036D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     36     53      1     0     0   9.737D-03  -2.479D+00\n",
      "  F =  -2.4788709523875174     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-30 23:00:00\n",
      "24 : predicted = [0.46 0.38 0.34 0.32] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47622D+00    |proj g|=  2.02204D+00\n",
      "\n",
      "At iterate    5    f= -2.47663D+00    |proj g|=  4.40399D-01\n",
      "\n",
      "At iterate   10    f= -2.47861D+00    |proj g|=  6.93887D-01\n",
      "\n",
      "At iterate   15    f= -2.47881D+00    |proj g|=  5.67747D-02\n",
      "  ys=-4.837E-10  -gs= 9.010E-10 BFGS update SKIPPED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      "\n",
      " Line search cannot locate an adequate point after MAXLS\n",
      "  function and gradient evaluations.\n",
      "  Previous x, f and g restored.\n",
      " Possible causes: 1 error in function or gradient evaluation;\n",
      "                  2 rounding error dominate computation.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     18     69      2     1     0   3.287D-02  -2.479D+00\n",
      "  F =  -2.4788071430960050     \n",
      "\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH                              \n",
      "2014-12-31 00:00:00\n",
      "25 : predicted = [0.38 0.34 0.33 0.32] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47614D+00    |proj g|=  2.00057D+00\n",
      "\n",
      "At iterate    5    f= -2.47656D+00    |proj g|=  5.49223D-01\n",
      "\n",
      "At iterate   10    f= -2.47869D+00    |proj g|=  5.94122D-02\n",
      "\n",
      "At iterate   15    f= -2.47869D+00    |proj g|=  2.58682D-02\n",
      "\n",
      "At iterate   20    f= -2.47871D+00    |proj g|=  1.24320D-01\n",
      "\n",
      "At iterate   25    f= -2.47875D+00    |proj g|=  1.51474D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     29     78      2     0     0   2.094D-02  -2.479D+00\n",
      "  F =  -2.4787583392322947     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 01:00:00\n",
      "26 : predicted = [0.36 0.34 0.34 0.38] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47614D+00    |proj g|=  1.99668D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47653D+00    |proj g|=  3.66901D-01\n",
      "\n",
      "At iterate   10    f= -2.47809D+00    |proj g|=  2.24444D+00\n",
      "\n",
      "At iterate   15    f= -2.47868D+00    |proj g|=  2.63075D-02\n",
      "\n",
      "At iterate   20    f= -2.47875D+00    |proj g|=  4.81122D-02\n",
      "\n",
      "At iterate   25    f= -2.47875D+00    |proj g|=  1.83485D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     25     58      1     0     0   1.835D-02  -2.479D+00\n",
      "  F =  -2.4787497011028790     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 02:00:00\n",
      "27 : predicted = [0.32 0.32 0.35 0.46] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47671D+00    |proj g|=  1.93928D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47709D+00    |proj g|=  3.91680D-01\n",
      "\n",
      "At iterate   10    f= -2.47903D+00    |proj g|=  2.10161D-01\n",
      "\n",
      "At iterate   15    f= -2.47920D+00    |proj g|=  8.97482D-03\n",
      "\n",
      "At iterate   20    f= -2.47924D+00    |proj g|=  6.23145D-02\n",
      "\n",
      "At iterate   25    f= -2.47924D+00    |proj g|=  1.18366D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     25     40      1     0     0   1.184D-02  -2.479D+00\n",
      "  F =  -2.4792372511307410     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 03:00:00\n",
      "28 : predicted = [0.32 0.36 0.47 0.63] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47680D+00    |proj g|=  1.96853D+00\n",
      "\n",
      "At iterate    5    f= -2.47718D+00    |proj g|=  3.70709D-01\n",
      "\n",
      "At iterate   10    f= -2.47924D+00    |proj g|=  1.31867D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     13     52      2     0     0   1.460D-02  -2.479D+00\n",
      "  F =  -2.4792618517069758     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-12-31 04:00:00\n",
      "29 : predicted = [0.37 0.48 0.65 0.75] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47675D+00    |proj g|=  1.91395D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47711D+00    |proj g|=  3.52215D-01\n",
      "\n",
      "At iterate   10    f= -2.47901D+00    |proj g|=  1.56768D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     14     21      1     0     0   1.306D-02  -2.479D+00\n",
      "  F =  -2.4792591005440032     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 05:00:00\n",
      "30 : predicted = [0.48 0.64 0.75 0.8 ] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47691D+00    |proj g|=  1.93197D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47727D+00    |proj g|=  3.68324D-01\n",
      "\n",
      "At iterate   10    f= -2.47919D+00    |proj g|=  1.43057D+00\n",
      "\n",
      "At iterate   15    f= -2.47938D+00    |proj g|=  2.54958D-02\n",
      "\n",
      "At iterate   20    f= -2.47944D+00    |proj g|=  2.57447D-02\n",
      "\n",
      "At iterate   25    f= -2.47944D+00    |proj g|=  2.67338D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     29     73      2     0     0   2.628D-02  -2.479D+00\n",
      "  F =  -2.4794384832100911     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 06:00:00\n",
      "31 : predicted = [0.63 0.73 0.79 0.81] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47673D+00    |proj g|=  1.92357D+00\n",
      "\n",
      "At iterate    5    f= -2.47710D+00    |proj g|=  4.18150D-01\n",
      "\n",
      "At iterate   10    f= -2.47868D+00    |proj g|=  2.30064D+00\n",
      "\n",
      "At iterate   15    f= -2.47921D+00    |proj g|=  2.43921D-02\n",
      "\n",
      "At iterate   20    f= -2.47922D+00    |proj g|=  3.58191D-02\n",
      "\n",
      "At iterate   25    f= -2.47928D+00    |proj g|=  1.65580D-01\n",
      "\n",
      "At iterate   30    f= -2.47928D+00    |proj g|=  1.85797D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     31     51      1     0     0   1.645D-03  -2.479D+00\n",
      "  F =  -2.4792829852960212     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 07:00:00\n",
      "32 : predicted = [0.71 0.76 0.79 0.81] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47694D+00    |proj g|=  1.78907D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47726D+00    |proj g|=  3.68642D-01\n",
      "\n",
      "At iterate   10    f= -2.47873D+00    |proj g|=  2.22035D+00\n",
      "\n",
      "At iterate   15    f= -2.47929D+00    |proj g|=  1.17177D-02\n",
      "\n",
      "At iterate   20    f= -2.47935D+00    |proj g|=  5.91209D-02\n",
      "  ys=-4.250E-09  -gs= 8.301E-08 BFGS update SKIPPED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   25    f= -2.47935D+00    |proj g|=  5.78090D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     25     61      1     1     0   5.781D-02  -2.479D+00\n",
      "  F =  -2.4793541605819605     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 08:00:00\n",
      "33 : predicted = [0.79 0.82 0.83 0.81] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47736D+00    |proj g|=  1.79643D+00\n",
      "\n",
      "At iterate    5    f= -2.47767D+00    |proj g|=  3.32897D-01\n",
      "\n",
      "At iterate   10    f= -2.47926D+00    |proj g|=  2.08773D+00\n",
      "\n",
      "At iterate   15    f= -2.47971D+00    |proj g|=  9.57875D-03\n",
      "\n",
      "At iterate   20    f= -2.47971D+00    |proj g|=  1.56751D-01\n",
      "\n",
      "At iterate   25    f= -2.47977D+00    |proj g|=  3.37333D-02\n",
      "\n",
      "At iterate   30    f= -2.47978D+00    |proj g|=  6.69309D-02\n",
      "\n",
      "At iterate   35    f= -2.47978D+00    |proj g|=  2.86920D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     35     53      1     0     0   2.869D-02  -2.480D+00\n",
      "  F =  -2.4797774782822741     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 09:00:00\n",
      "34 : predicted = [0.82 0.83 0.81 0.79] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47794D+00    |proj g|=  1.89004D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47829D+00    |proj g|=  3.41610D-01\n",
      "\n",
      "At iterate   10    f= -2.48037D+00    |proj g|=  9.91250D-02\n",
      "\n",
      "At iterate   15    f= -2.48041D+00    |proj g|=  3.39632D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   20    f= -2.48043D+00    |proj g|=  8.73631D-04\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     20     39      1     0     0   8.736D-04  -2.480D+00\n",
      "  F =  -2.4804277282476082     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 10:00:00\n",
      "35 : predicted = [0.8  0.78 0.76 0.75] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47788D+00    |proj g|=  1.87734D+00\n",
      "\n",
      "At iterate    5    f= -2.47867D+00    |proj g|=  1.47858D+00\n",
      "\n",
      "At iterate   10    f= -2.48030D+00    |proj g|=  2.90576D-02\n",
      "\n",
      "At iterate   15    f= -2.48040D+00    |proj g|=  1.67454D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     18     34      1     0     0   9.372D-03  -2.480D+00\n",
      "  F =  -2.4804030990475883     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 11:00:00\n",
      "36 : predicted = [0.77 0.75 0.74 0.75] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47767D+00    |proj g|=  1.89557D+00\n",
      "\n",
      "At iterate    5    f= -2.47803D+00    |proj g|=  5.52827D-01\n",
      "\n",
      "At iterate   10    f= -2.48018D+00    |proj g|=  3.15775D-02\n",
      "\n",
      "At iterate   15    f= -2.48019D+00    |proj g|=  7.28922D-02\n",
      "\n",
      "At iterate   20    f= -2.48024D+00    |proj g|=  4.03181D-02\n",
      "\n",
      "At iterate   25    f= -2.48026D+00    |proj g|=  2.66339D-02\n",
      "\n",
      "At iterate   30    f= -2.48026D+00    |proj g|=  1.88981D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     30     62      1     0     0   1.890D-02  -2.480D+00\n",
      "  F =  -2.4802607634080278     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 12:00:00\n",
      "37 : predicted = [0.77 0.76 0.76 0.79] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47794D+00    |proj g|=  1.87254D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47829D+00    |proj g|=  5.50248D-01\n",
      "\n",
      "At iterate   10    f= -2.48001D+00    |proj g|=  1.36974D+00\n",
      "\n",
      "At iterate   15    f= -2.48050D+00    |proj g|=  1.23945D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  ys=-1.559E-11  -gs= 5.189E-10 BFGS update SKIPPED\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     20     75      3     1     0   7.571D-03  -2.480D+00\n",
      "  F =  -2.4804978870406313     \n",
      "\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH                              \n",
      "2014-12-31 13:00:00\n",
      "38 : predicted = [0.75 0.75 0.78 0.89] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Line search cannot locate an adequate point after MAXLS\n",
      "  function and gradient evaluations.\n",
      "  Previous x, f and g restored.\n",
      " Possible causes: 1 error in function or gradient evaluation;\n",
      "                  2 rounding error dominate computation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47799D+00    |proj g|=  1.88498D+00\n",
      "\n",
      "At iterate    5    f= -2.47833D+00    |proj g|=  3.89752D-01\n",
      "\n",
      "At iterate   10    f= -2.48033D+00    |proj g|=  1.50550D+00\n",
      "\n",
      "At iterate   15    f= -2.48054D+00    |proj g|=  1.38503D-02\n",
      "  ys=-3.940E-07  -gs= 7.542E-07 BFGS update SKIPPED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n",
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   20    f= -2.48054D+00    |proj g|=  6.76348D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     20     51      1     1     0   6.763D-02  -2.481D+00\n",
      "  F =  -2.4805420620341851     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 14:00:00\n",
      "39 : predicted = [0.73 0.75 0.87 0.98] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47785D+00    |proj g|=  1.77092D+00\n",
      "\n",
      "At iterate    5    f= -2.47815D+00    |proj g|=  3.53256D-01\n",
      "\n",
      "At iterate   10    f= -2.48041D+00    |proj g|=  2.52881D-01\n",
      "\n",
      "At iterate   15    f= -2.48047D+00    |proj g|=  4.25982D-02\n",
      "\n",
      "At iterate   20    f= -2.48048D+00    |proj g|=  8.84217D-02\n",
      "\n",
      "At iterate   25    f= -2.48049D+00    |proj g|=  8.98063D-02\n",
      "\n",
      "At iterate   30    f= -2.48050D+00    |proj g|=  6.52910D-02\n",
      "  ys=-2.018E-07  -gs= 4.158E-08 BFGS update SKIPPED\n",
      "\n",
      "At iterate   35    f= -2.48051D+00    |proj g|=  1.68844D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     35     80      1     1     0   1.688D-02  -2.481D+00\n",
      "  F =  -2.4805051905639544     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-12-31 15:00:00\n",
      "40 : predicted = [0.74 0.85 0.96 0.95] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47838D+00    |proj g|=  1.83840D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47871D+00    |proj g|=  3.63060D-01\n",
      "\n",
      "At iterate   10    f= -2.48046D+00    |proj g|=  2.30629D+00\n",
      "\n",
      "At iterate   15    f= -2.48097D+00    |proj g|=  1.65840D-01\n",
      "\n",
      "At iterate   20    f= -2.48098D+00    |proj g|=  5.90779D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     24     38      1     0     0   3.551D-02  -2.481D+00\n",
      "  F =  -2.4809922528864514     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 16:00:00\n",
      "41 : predicted = [0.83 0.94 0.93 0.88] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47906D+00    |proj g|=  1.91053D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47942D+00    |proj g|=  3.91042D-01\n",
      "\n",
      "At iterate   10    f= -2.48158D+00    |proj g|=  9.54069D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     13     25      1     0     0   1.321D-02  -2.482D+00\n",
      "  F =  -2.4816740232701244     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 17:00:00\n",
      "42 : predicted = [0.94 0.93 0.88 0.82] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47776D+00    |proj g|=  1.89886D+00\n",
      "\n",
      "At iterate    5    f= -2.47812D+00    |proj g|=  4.21911D-01\n",
      "\n",
      "At iterate   10    f= -2.47982D+00    |proj g|=  2.37040D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     14     22      1     0     0   1.070D-02  -2.480D+00\n",
      "  F =  -2.4804056218650330     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 18:00:00\n",
      "43 : predicted = [0.87 0.82 0.77 0.66] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47704D+00    |proj g|=  1.89328D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47739D+00    |proj g|=  3.83344D-01\n",
      "\n",
      "At iterate   10    f= -2.47894D+00    |proj g|=  2.42868D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -2.47969D+00    |proj g|=  9.33829D-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      "\n",
      " Line search cannot locate an adequate point after MAXLS\n",
      "  function and gradient evaluations.\n",
      "  Previous x, f and g restored.\n",
      " Possible causes: 1 error in function or gradient evaluation;\n",
      "                  2 rounding error dominate computation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     16     83      3     0     0   9.338D-03  -2.480D+00\n",
      "  F =  -2.4796939099209014     \n",
      "\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH                              \n",
      "2014-12-31 19:00:00\n",
      "44 : predicted = [0.79 0.73 0.63 0.49] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -2.47692D+00    |proj g|=  1.83520D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -2.47725D+00    |proj g|=  3.52916D-01\n",
      "\n",
      "At iterate   10    f= -2.47917D+00    |proj g|=  1.89581D+00\n",
      "\n",
      "At iterate   15    f= -2.47952D+00    |proj g|=  1.46474D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4     15     25      1     0     0   1.465D-02  -2.480D+00\n",
      "  F =  -2.4795157930919371     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "2014-12-31 20:00:00\n",
      "45 : predicted = [0.7  0.59 0.46 0.35] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
      "CPU times: user 5min 3s, sys: 3min 5s, total: 8min 8s\n",
      "Wall time: 1min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
    "\n",
    "train_ts = train['load']\n",
    "test_ts = test_shifted\n",
    "\n",
    "history = [x for x in train_ts]\n",
    "history = history[(-training_window):]\n",
    "\n",
    "predictions = list()\n",
    "\n",
    "# let's user simpler model for demonstration\n",
    "order = (2, 1, 0)\n",
    "seasonal_order = (1, 1, 0, 24)\n",
    "\n",
    "for t in range(test_ts.shape[0]):\n",
    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
    "    model_fit = model.fit()\n",
    "    yhat = model_fit.forecast(steps = HORIZON+1)\n",
    "    predictions.append(yhat)\n",
    "    obs = list(test_ts.iloc[t])\n",
    "    # move the training window\n",
    "    history.append(obs[0])\n",
    "    history.pop(0)\n",
    "    print(test_ts.index[t])\n",
    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare predictions to actual load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>h</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-30 00:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,009.18</td>\n",
       "      <td>3,023.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-30 01:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,955.79</td>\n",
       "      <td>2,935.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-12-30 02:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,900.11</td>\n",
       "      <td>2,899.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-12-30 03:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,917.78</td>\n",
       "      <td>2,886.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-30 04:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,947.03</td>\n",
       "      <td>2,963.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    h  prediction   actual\n",
       "0 2014-12-30 00:00:00  t+1    3,009.18 3,023.00\n",
       "1 2014-12-30 01:00:00  t+1    2,955.79 2,935.00\n",
       "2 2014-12-30 02:00:00  t+1    2,900.11 2,899.00\n",
       "3 2014-12-30 03:00:00  t+1    2,917.78 2,886.00\n",
       "4 2014-12-30 04:00:00  t+1    2,947.03 2,963.00"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+2)])\n",
    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON]\n",
    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the **mean absolute percentage error (MAPE)** over all predictions\n",
    "\n",
    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h\n",
      "t+1   0.01\n",
      "t+2   0.01\n",
      "t+3   0.02\n",
      "t+4   0.02\n",
      "Name: APE, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "if(HORIZON > 1):\n",
    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
    "    print(eval_df.groupby('h')['APE'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One step forecast MAPE:  0.5420260186331665 %\n"
     ]
    }
   ],
   "source": [
    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-step forecast MAPE:  1.286865092111503 %\n"
     ]
    }
   ],
   "source": [
    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the predictions vs the actuals for the first week of the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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   "source": [
    "if(HORIZON == 1):\n",
    "    ## Plotting single step forecast\n",
    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
    "\n",
    "else:\n",
    "    ## Plotting multi step forecast\n",
    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
    "    for t in range(1, HORIZON+1):\n",
    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
    "\n",
    "    fig = plt.figure(figsize=(15, 8))\n",
    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
    "    ax = fig.add_subplot(111)\n",
    "    for t in range(1, HORIZON+1):\n",
    "        x = plot_df['timestamp'][(t-1):]\n",
    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
    "    \n",
    "    ax.legend(loc='best')\n",
    "    \n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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