diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md index bd6d64f43..6dd00b0e4 100644 --- a/8-Reinforcement/1-QLearning/README.md +++ b/8-Reinforcement/1-QLearning/README.md @@ -31,7 +31,7 @@ Each cell in this board can either be: * **an apple**, which represents something Peter would be glad to find in order to feed himself * **a wolf**, which is dangerous and should be avoided -There is a separate Python module, [`rlboard.py`](rlboard.py), which contains the code to work with this environment. Because this code is not important for understanding our concepts, we will just import the module and use it to create the sample board: +There is a separate Python module, [`rlboard.py`](rlboard.py), which contains the code to work with this environment. Because this code is not important for understanding our concepts, we will just import the module and use it to create the sample board (code block 1): ```python from rlboard import * @@ -45,7 +45,8 @@ This code should print the picture of the environment similar to the one above. ## Actions and Policy -In our example, Peter's goal would be to find an apple, while avoiding the wolf and other obstacles. To do this, he can essentially walk around until he finds and apple. Therefore, at any position he can chose between one of the following actions: up, down, left and right. We will define those actions as a dictionary, and map them to pairs of corresponding coordinate changes. For example, moving right (`R`) would correspond to a pair `(1,0)`. +In our example, Peter's goal would be to find an apple, while avoiding the wolf and other obstacles. To do this, he can essentially walk around until he finds and apple. Therefore, at any position he can chose between one of the following actions: up, down, left and right. We will define those actions as a dictionary, and map them to pairs of corresponding coordinate changes. For example, moving right (`R`) would correspond to a pair `(1,0)`. (code block 2) + ```python actions = { "U" : (0,-1), "D" : (0,1), "L" : (-1,0), "R" : (1,0) } action_idx = { a : i for i,a in enumerate(actions.keys()) } @@ -57,7 +58,7 @@ The goal of reinforcement learning is to eventually learn a good policy that wil ## Random walk -Let's first solve our problem by implementing a random walk strategy. With random walk, we will randomly chose the next action from allowed ones, until we reach the apple. +Let's first solve our problem by implementing a random walk strategy. With random walk, we will randomly chose the next action from allowed ones, until we reach the apple (code block 3). ```python def random_policy(m): @@ -86,7 +87,7 @@ def walk(m,policy,start_position=None): walk(m,random_policy) ``` -The call to `walk` should return us the length of corresponding path, which can vary from one run to another. We can run the walk experiment a number of times (say, 100), and print the resulting statistics: +The call to `walk` should return us the length of corresponding path, which can vary from one run to another. We can run the walk experiment a number of times (say, 100), and print the resulting statistics (code block 4): ```python def print_statistics(policy): @@ -111,7 +112,7 @@ You can also see how Peter's movement looks like during random walk: ## Reward Function -To make out policy more intelligent, we need to understand which moves are "better" than others. To do this, we need to define our goal. The goal can be defined in terms of **reward function**, that will return some score value for each state. The higher the number - the better is the reward function +To make out policy more intelligent, we need to understand which moves are "better" than others. To do this, we need to define our goal. The goal can be defined in terms of **reward function**, that will return some score value for each state. The higher the number - the better is the reward function. (code block 5) ```python move_reward = -0.1 @@ -130,13 +131,13 @@ def reward(m,pos=None): return move_reward ``` -Interesting thing about reward function is that in most of the cases *we are only given substantial reward at the end of the game*. It means that out algorithm should somehow remember "good" steps that lead to positive reward at the end, and increase their importance. Similarly, all moves that lead to bad results should be discouraged. +An interesting thing about reward function is that in most of the cases *we are only given substantial reward at the end of the game*. It means that out algorithm should somehow remember "good" steps that lead to positive reward at the end, and increase their importance. Similarly, all moves that lead to bad results should be discouraged. ## Q-Learning An algorithm that we will discuss here is called **Q-Learning**. In this algorithm, the policy is defined by a function (or a data structure) called **Q-Table**. It records the "goodness" of each of the actions in a given state. -It is called Q-Table because it is often convenient to represent it as a table, or multi-dimensional array. Since our board has dimensions `width` x `height`, we can represent Q-Table by a numpy array with shape `width` x `height` x `len(actions)`: +It is called Q-Table because it is often convenient to represent it as a table, or multi-dimensional array. Since our board has dimensions `width` x `height`, we can represent Q-Table by a numpy array with shape `width` x `height` x `len(actions)`: (code block 6) ```python Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions) @@ -190,7 +191,7 @@ In the algorithm above, we did not specify how exactly we should choose an actio Thus, the best approach is to balance between exploration and exploitation. This can be done by choosing the action at state *s* with probabilities proportional to values in Q-Table. In the beginning, when Q-Table values are all the same, it would correspond to a random selection, but as we learn more about our environment, we would be more likely to follow the optimal route while allowing the agent to choose the unexplored path once in a while. ## Python Implementation -Now we are ready to implement the learning algorithm. Before that, we also need some function that will convert arbitrary numbers in the Q-Table into a vector of probabilities for corresponding actions: +Now we are ready to implement the learning algorithm. Before that, we also need some function that will convert arbitrary numbers in the Q-Table into a vector of probabilities for corresponding actions: (code block 7) ```python def probs(v,eps=1e-4): @@ -201,7 +202,7 @@ def probs(v,eps=1e-4): We add a few `eps` to the original vector in order to avoid division by 0 in the initial case, when all components of the vector are identical. -The actual learning algorithm we will run for 5000 experiments, also called **epochs**: +The actual learning algorithm we will run for 5000 experiments, also called **epochs**: (code block 8) ```python for epoch in range(5000): @@ -236,7 +237,7 @@ After executing this algorithm, Q-Table should be updated with values that defin ## Checking the Policy -Since Q-Table lists the "attractiveness" of each action at each state, it is quite easy to use it to define the efficient navigation in our world. In the simplest case, we can select the action corresponding to the highest Q-Table value: +Since Q-Table lists the "attractiveness" of each action at each state, it is quite easy to use it to define the efficient navigation in our world. In the simplest case, we can select the action corresponding to the highest Q-Table value: (code block 9) ```python def qpolicy_strict(m): @@ -258,7 +259,7 @@ walk(m,qpolicy_strict) ## Navigation -Better navigation policy would be the one that we have used during training, which combines exploitation and exploration. In this policy, we will select each action with a certain probability, proportional to the values in Q-Table. This strategy may still result in the agent returning back to the position it has already explored, but, as you can see from the code below, it results in very short average path to the desired location (remember that `print_statistics` runs the simulation 100 times): +Better navigation policy would be the one that we have used during training, which combines exploitation and exploration. In this policy, we will select each action with a certain probability, proportional to the values in Q-Table. This strategy may still result in the agent returning back to the position it has already explored, but, as you can see from the code below, it results in very short average path to the desired location (remember that `print_statistics` runs the simulation 100 times): (code block 10) ```python def qpolicy(m): diff --git a/8-Reinforcement/1-QLearning/notebook.ipynb b/8-Reinforcement/1-QLearning/notebook.ipynb index 6dee19c93..4785bc7e8 100644 --- a/8-Reinforcement/1-QLearning/notebook.ipynb +++ b/8-Reinforcement/1-QLearning/notebook.ipynb @@ -28,9 +28,9 @@ "source": [ "# Peter and the Wolf: Reinforcement Learning Primer\n", "\n", - "In this tutorial, we will learn how to apply Reinforcement learning to a problem of path finding. The setting is inspired by [Peter and the Wolf](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) musical fairy tale by Russian composer [Segei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). It is a story about young pioneer Peter, who bravely goes out of his house to the forest clearing to chase the wolf. We will train machine learning algorithms that will help Peter to explore the surroinding area and build an optimal navigation map.\n", + "In this tutorial, we will learn how to apply Reinforcement learning to a problem of path finding. The setting is inspired by [Peter and the Wolf](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) musical fairy tale by Russian composer [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). It is a story about young pioneer Peter, who bravely goes out of his house to the forest clearing to chase a wolf. We will train machine learning algorithms that will help Peter to explore the surrounding area and build an optimal navigation map.\n", "\n", - "First, let's import a bunch of userful libraries:" + "First, let's import a bunch of useful libraries:" ], "cell_type": "markdown", "metadata": {} @@ -58,23 +58,16 @@ "For simplicity, let's consider Peter's world to be a square board of size `width` x `height`. Each cell in this board can either be:\n", "* **ground**, on which Peter and other creatures can walk\n", "* **water**, on which you obviously cannot walk\n", - "* **a tree** or **grass** - a place where you cat take some rest\n", + "* **a tree** or **grass** - a place where you can rest\n", "* **an apple**, which represents something Peter would be glad to find in order to feed himself\n", "* **a wolf**, which is dangerous and should be avoided\n", "\n", "To work with the environment, we will define a class called `Board`. In order not to clutter this notebook too much, we have moved all code to work with the board into separate `rlboard` module, which we will now import. You may look inside this module to get more details about the internals of the implementation." ], - "cell_type": "markdown", - "metadata": {} - }, - { "cell_type": "code", - "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [ - "from rlboard import *" - ] + "execution_count": null, + "outputs": [] }, { "source": [ @@ -101,10 +94,7 @@ } ], "source": [ - "width, height = 8,8\n", - "m = Board(width,height)\n", - "m.randomize(seed=13)\n", - "m.plot()" + "# code block 1" ] }, { @@ -122,13 +112,12 @@ "metadata": {}, "outputs": [], "source": [ - "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n", - "action_idx = { a : i for i,a in enumerate(actions.keys()) }" + "# code block 2" ] }, { "source": [ - "The strategy of our agent (Peter) is defined by so-called **policy**. A policy is a function that returns the action at any given state. In our case, the state of the problem is represented by the board, including the current position of the player. \n", + "The strategy of our agent (Peter) is defined by a so-called **policy**. A policy is a function that returns the action at any given state. In our case, the state of the problem is represented by the board, including the current position of the player. \n", "\n", "The goal of reinforcement learning is to eventually learn a good policy that will allow us to solve the problem efficiently. However, as a baseline, let's consider the simplest policy called **random walk**.\n", "\n", @@ -139,57 +128,14 @@ "cell_type": "markdown", "metadata": {} }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "18" - ] - }, - "metadata": {}, - "execution_count": 5 - } - ], - "source": [ - "def random_policy(m):\n", - " return random.choice(list(actions))\n", - "\n", - "def walk(m,policy,start_position=None):\n", - " n = 0 # number of steps\n", - " # set initial position\n", - " if start_position:\n", - " m.human = start_position \n", - " else:\n", - " m.random_start()\n", - " while True:\n", - " if m.at() == Board.Cell.apple:\n", - " return n # success!\n", - " if m.at() in [Board.Cell.wolf, Board.Cell.water]:\n", - " return -1 # eaten by wolf or drowned\n", - " while True:\n", - " a = actions[policy(m)]\n", - " new_pos = m.move_pos(m.human,a)\n", - " if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:\n", - " m.move(a) # do the actual move\n", - " break\n", - " n+=1\n", - "\n", - "walk(m,random_policy)" - ] - }, { "source": [ - "Let's run random walk experiment several times and see the average number of steps taken:" + "# Let's run a random walk experiment several times and see the average number of steps taken: code block 3" ], - "cell_type": "markdown", - "metadata": {} + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -205,25 +151,14 @@ } ], "source": [ - "def print_statistics(policy):\n", - " s,w,n = 0,0,0\n", - " for _ in range(100):\n", - " z = walk(m,policy)\n", - " if z<0:\n", - " w+=1\n", - " else:\n", - " s += z\n", - " n += 1\n", - " print(f\"Average path length = {s/n}, eaten by wolf: {w} times\")\n", - "\n", - "print_statistics(random_policy)" + "# code block 4" ] }, { "source": [ "## Reward Function\n", "\n", - "To make out policy more intelligent, we need to understand which moves are \"better\" than others. To do this, we need to define our goal. The goal can be defined in terms of **reward function**, that will return some score value for each state. The higher the number - the better is the reward function\n", + "To make our policy more intelligent, we need to understand which moves are \"better\" than others. To do this, we need to define our goal. The goal can be defined in terms of **reward function**, that will return some score value for each state. The higher the number - the better the reward function is.\n", "\n" ], "cell_type": "markdown", @@ -235,25 +170,12 @@ "metadata": {}, "outputs": [], "source": [ - "move_reward = -0.1\n", - "goal_reward = 10\n", - "end_reward = -10\n", - "\n", - "def reward(m,pos=None):\n", - " pos = pos or m.human\n", - " if not m.is_valid(pos):\n", - " return end_reward\n", - " x = m.at(pos)\n", - " if x==Board.Cell.water or x == Board.Cell.wolf:\n", - " return end_reward\n", - " if x==Board.Cell.apple:\n", - " return goal_reward\n", - " return move_reward" + "#code block 5" ] }, { "source": [ - "Interesting thing about reward function is that in most of the cases *we are only given substantial reward at the end of the game*. It means that out algorithm should somehow remember \"good\" steps that lead to positive reward at the end, and increase their importance. Similarly, all moves that lead to bad results should be discouraged.\n", + "An interesting thing about the reward function is that in most of the cases *we are only given substantial reward at the end of the game*. It means that our algorithm should somehow remember \"good\" steps that lead to a positive reward at the end and increase their importance. Similarly, all moves that lead to bad results should be discouraged.\n", "\n", "## Q-Learning\n", "\n", @@ -270,7 +192,7 @@ "metadata": {}, "outputs": [], "source": [ - "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)" + "# code block 6" ] }, { @@ -301,13 +223,6 @@ "m.plot(Q)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "source": [ "In the center of each cell there is an \"arrow\" that indicates the preferred direction of movement. Since all directions are equal, a dot is displayed.\n", @@ -320,7 +235,7 @@ "\n", "> It is not the immediate result that matters, but rather the final result, which we will obtain at the end of the simulation.\n", "\n", - "In order to account for this delayed reward, we need to use the principles of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, which allows us to think about out problem recursively.\n", + "In order to account for this delayed reward, we need to use the principles of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, which allows us to think about our problem recursively.\n", "\n", "Suppose we are now at the state $s$, and we want to move to the next state $s'$. By doing so, we will receive the immediate reward $r(s,a)$, defined by reward function, plus some future reward. If we suppose that our Q-Table correctly reflects the \"attractiveness\" of each action, then at state $s'$ we will chose an action $a'$ that corresponds to maximum value of $Q(s',a')$. Thus, the best possible future reward we could get at state $s'$ will be defined as $\\max_{a'}Q(s',a')$ (maximum here is computed over all possible actions $a'$ at state $s'$). \n", "\n", @@ -366,10 +281,7 @@ "metadata": {}, "outputs": [], "source": [ - "def probs(v,eps=1e-4):\n", - " v = v-v.min()+eps\n", - " v = v/v.sum()\n", - " return v" + "# code block 7" ] }, { @@ -400,41 +312,17 @@ "\n", "lpath = []\n", "\n", - "for epoch in range(10000):\n", - " clear_output(wait=True)\n", - " print(f\"Epoch = {epoch}\",end='')\n", - "\n", - " # Pick initial point\n", - " m.random_start()\n", - " \n", - " # Start travelling\n", - " n=0\n", - " cum_reward = 0\n", - " while True:\n", - " x,y = m.human\n", - " v = probs(Q[x,y])\n", - " a = random.choices(list(actions),weights=v)[0]\n", - " dpos = actions[a]\n", - " m.move(dpos)\n", - " r = reward(m)\n", - " cum_reward += r\n", - " if r==end_reward or cum_reward < -1000:\n", - " print(f\" {n} steps\",end='\\r')\n", - " lpath.append(n)\n", - " break\n", - " alpha = np.exp(-n / 3000)\n", - " gamma = 0.5\n", - " ai = action_idx[a]\n", - " Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n", - " n+=1" + "# code block 8" ] }, { "source": [ - "After executing this algorithm, Q-Table should be updated with values that define the attractiveness of different actions at each step. We can try to visualize Q-Table by plotting a vector at each cell that will point in the desired direction of movement. For simplicity, we draw small circle instead of arrow head." + "After executing this algorithm, the Q-Table should be updated with values that define the attractiveness of different actions at each step. We can try to visualize the Q-Table by plotting a vector at each cell that will point in the desired direction of movement. For simplicity, we draw a small circle instead of arrow head." ], - "cell_type": "markdown", - "metadata": {} + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -483,18 +371,12 @@ } ], "source": [ - "def qpolicy_strict(m):\n", - " x,y = m.human\n", - " v = probs(Q[x,y])\n", - " a = list(actions)[np.argmax(v)]\n", - " return a\n", - "\n", - "walk(m,qpolicy_strict)" + "# code block 9" ] }, { "source": [ - "If you try the code above several times, you may notice that sometimes it just \"hangs\", and you need to press STOP button in the notebook to interrupt it. This happens because there could be situations when two states \"point\" to each other in terms of optimal Q-Value, in which case the agents ends up moving between those states indefinitely.\n", + "If you try the code above several times, you may notice that sometimes it just \"hangs\", and you need to press the STOP button in the notebook to interrupt it. This happens because there could be situations when two states \"point\" to each other in terms of optimal Q-Value, in which case the agents ends up moving between those states indefinitely.\n", "\n", "> **Task 1:** Modify the `walk` function to limit the maximum length of path by a certain number of steps (say, 100), and watch the code above return this value from time to time.\n", "\n", @@ -502,8 +384,10 @@ "\n", "Better navigation policy would be the one that we have used during training, which combines exploitation and exploration. In this policy, we will select each action with a certain probability, proportional to the values in Q-Table. This strategy may still result in the agent returning back to the position it has already explored, but, as you can see from the code below, it results in very short average path to the desired location (remember that `print_statistics` runs the simulation 100 times): " ], - "cell_type": "markdown", - "metadata": {} + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -520,13 +404,7 @@ ], "source": [ "\n", - "def qpolicy(m):\n", - " x,y = m.human\n", - " v = probs(Q[x,y])\n", - " a = random.choices(list(actions),weights=v)[0]\n", - " return a\n", - "\n", - "print_statistics(qpolicy)" + "# code block 10" ] }, { diff --git a/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb index 82726489e..e8b208fad 100644 --- a/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb +++ b/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb @@ -10,12 +10,13 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.0" }, "orig_nbformat": 2, "kernelspec": { - "name": "python3", - "display_name": "Python 3.7.4 64-bit ('base': conda)" + "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7", + "display_name": "Python 3.7.3 64-bit", + "language": "python" }, "interpreter": { "hash": "c77bccf6af5544921fca6eddbefe5e7c44ddf71c61b63c74bd828ca1d0e389a0" @@ -41,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -54,15 +55,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" 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}, "metadata": { "needs_background": "light" @@ -78,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "metadata": { "tags": [] }, @@ -155,7 +156,7 @@ ] }, "metadata": {}, - "execution_count": 17 + "execution_count": 5 } ], "source": [ @@ -182,14 +183,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Killed by wolf = 5, won: 4 times, drown: 91 times\n" + "Killed by wolf = 5, won: 1 times, drown: 94 times\n" ] } ], @@ -218,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -242,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -251,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -313,15 +314,15 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -343,14 +344,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 12, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Killed by wolf = 32, won: 20 times, drown: 48 times\n" + "Killed by wolf = 1, won: 9 times, drown: 90 times\n" ] } ], @@ -373,25 +374,25 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "[]" + "[]" ] }, "metadata": {}, - "execution_count": 27 + "execution_count": 13 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" 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\n" }, "metadata": { "needs_background": "light" diff --git a/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/8-Reinforcement/1-QLearning/solution/notebook.ipynb index 6dee19c93..45f9a6732 100644 --- a/8-Reinforcement/1-QLearning/solution/notebook.ipynb +++ b/8-Reinforcement/1-QLearning/solution/notebook.ipynb @@ -10,15 +10,15 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.0" }, "orig_nbformat": 2, "kernelspec": { "name": "python3", - "display_name": "Python 3.7.4 64-bit ('base': conda)" + "display_name": "Python 3.7.0 64-bit ('3.7')" }, "interpreter": { - "hash": "c77bccf6af5544921fca6eddbefe5e7c44ddf71c61b63c74bd828ca1d0e389a0" + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" } }, "nbformat": 4, @@ -92,8 +92,8 @@ "output_type": "display_data", "data": { "text/plain": "
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\n" 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eSzQSYUHfAs5aeBYvOXIRG7ZvYPWmVQwXcgwOD5IZnkfp6SwndRxPZWwITJVgTPjLG/4SEaFarZLLDVPYuppnV9/LSL7KrEUX0XnEYgr5PG1tbWzfvh0RoVQq0dvbSzQaJRqN0tnZ2ehTotRe0xa3OqistbiuS1dXF57n8dU3fIWPp/6JHz/yY0aLo2QiGdKSoiYu24afYmxkjPZYBxcvvZhioUiKboa3b8Pp2oy71SMIfGKxGPfc9iW2rb+PkcEXOOWcv+EVF/0Nvl/fV6lU6OrqIggC0uk0Y2NjRCIRrLUUi0Wy2WyjT4tSe0WDWx10juPgOA7WWrpS3Xz6NZ8mJgl++NsfsDW3DTwQDyQQTpl9CqlIiucGnyMVTdEe6+Goucfxvf++mQXnb2H57f/BO193OQ/98kcMzJzNxe+5iYEjXzLx/uPD/CKRyMSokskTQ3QVO9WKNLjVQec4DsVikUwmQ6lUoiPRwWdf+y98+k8/wZ99+VJG8iOsfeE5+tt7yRWHaYu1Uy1XwbNs3z5MWyzDeaddxMaNz/Brexu/ed9yugLLBa96O/OOX0osFqNcLpNIJKjVaiSTSYrFIvF4HNd1SafTBEGAMYZYLNbo06HUXtPgVgfV+Djrnp4ecrkcnZ2dlEol4rE4btHlrqvuYn1uPf/1yH9RqpZwfIdMPE1+NA9WqJSrJCJx3vzqN3P6yafzq5X/zdfv/2f+5LVv5uSzXkcQBBSLRbq7u8nn82SzWUZHR+nt7aVQKJBKpRgeHiadTmOtpVQqNfUMP6V2RoNbHVQiQiKRIJfLkUqlGBsbIxaL4fs+bW1tWGtZ2L+Q95/3fqy1xKMRttz7C7b89sekE0l6XvWndC49l1giwcjICN4Wn8qo8LJXv4F4PI61ls7OTobWr+ehb/xfchufp+uo4znt8r+is79vor/bGIMxpqnXTVFqKhrc6qAab3Fns1nGxsbo6OigXC4TjUapVCpEo1Fwqzi1Kk/98/uxbpXZf/Y2Tv+H6zDiEIs4rFv2fxh+7BH8wLB2aJTE9m3UVj3Ew/f9im0rf4cXBBz/5is45dLLcGtVgmqN7135Dor5Ihf986fomH8UA3Pm4jgOpVKJRCLR6NOi1F7R4FYHXSQSwfO8iVmM4xcSI5EIQWGMzcs+T+n5tRz/t58m1t6BNzpC9bk1IFCzMOvStzPvnVfhlwrM+t8VnP7Mkwzf9yuOfMU5nPTWv8T3XUojI7iFMQILBstFH/skfmD49Xe+xcp77+U9//FNFpx6GpFIpNGnQ6m9psGtDioR2WEdkfE1Q6y14Pts+Op1BFs3s+Bt78XdvgV/+xYEy/jgD7HgPr+OqrUYoOPY4+lcfBqB61MZHSa/4VkCawksBNZirCUwYKzFN5ZTX3cRnjF85+/+lsuu+xxHn3lm406GUvtIg1sdVNZafN+nq6trh4uT0WiUF277NpW1TzL/7e8Fr4oYEAlvO7xHPcDBEpRLuNbWwzoM6MBYjGUivP3AEliDHx6z6OxXUau63Pi+9/A33/8hx596aoPOhlL7RoNbHVSO45BMJhkcHKSnp4ehoSEymQy1concL+7k2LddRVAewzqACE7YQnfC5LbW1lvnlnqCj4e0sRhj8a0hMJYgAD8Mbs8YfAu+MQRGCIzh+Je+jG0bN1IZGmrk6VBqn2hwq4NqvMWdSqXwPG/iwuDwvb8gnmmjOrSJiCM4kfpqDBKByKTgNrbeqrZGIDAYa7AWrAlb2mY8oC2eqXeP+MbiW+oBburdKJ5v6Jk9j6988AN8ffUTiPZ1qxaiwa0OuvHZiuP31loKv7uf9JELCSolxBGs49RX0nEEcYRImNzWWMRarAEb2HBYH+F9PbwDUw/pF4Pb4JkXg9sL6q3wI44+iqceerBRp0GpfabBrQ6q8fWzC4UC6XSaUqlEOp0mEnGwgUtQKeE4gnEcrEM9wCP18AbCJjdgDGY8uC34QT2U/aDe4vbDFrdnLJ4f4FuLayxeIHhBEIY4E1/EoFQr0eBWB5UxhlqtRmdnJ+VymY6ODlzXxa252OGtJMJ1TCQiOI4gEUEch3rz2+IDgTH1cA5sGND1x54NW9NBPbBdvx7O+fwYkXQGNxgP73B/OAlHqVajwa0OKsdxiMfjDA8P09fXx8jICO3t7SQ7sgz+78+IOw50dkIY3jj1ISW+W0MSKQzj3R9QKxUoD23HDQw13+AaSy0w1HxL4ESJ9g7gIYxt3kh6xixcY/ACqAUBvoHtg1twq9VGnxKl9poGtzqojDG4rktfX9/Et9a4rsvMS9/J9vtWMPr04wSz5pLp7cc4gnEEX8B/4Vlic47CApWtm/HyY1RrNarFIlU/wA0sFd9S8wOqgcFFMC88j0uE1Jy5jA0OIpkMXgDVwDCWy/Hc6idY/LpLQFcIVC1Gg1sddMaYie+JHF9mNXHEXEw0jlcqw7o1EATE29rwbEAEcPNjyMrf1sdqBwFeYHADgxu82D3iWxOO3QYvCKiO5qj5huGhISpegIvQMedIRkZG2LZpC1XX53Xve58u7apajga3OqhEhHg8TqFQIJFIUKlUJkI8SKRwjcV6AZH8GH7gEWx+IRwOKAgQYCcm2bjG4AeCayb3XZuJPm8/HGHiBx5BAJ4fUCkWyQ1uxVhAHFJtmUafEqX2mn51mTqoxr8Bp7Ozk0qlQnt7O8YYotEoR77tL6mF/dSlXI5ysUAtMFQDQyUwlAND1TdU/PpzN4Ba2OreoeVtTH3GpLETo0v8cPRJPjdS/0Z4x+GMN1yKJHV1QNV6tMWtDqrxZV2HhoZoa2tjdHSUeDyO53kc8bLz+L0BYw3GephCGXxTvz4p9TaGtSachAN+ONnGDS9WumZ8tIjFDer7vfEAtxZJJqlWavVjAp/Fr3wlcxcsaPAZUWrvaYtbHVTWWjzPo7e3l3K5TDabnfgmmkKpTPsZZ9db2X5AsVCk7NVb2GXPhI9tvcXtGyp+QCUcUVL1A2p+QC0IcH2LGwS4gZk0lttQKpZxay7tfX285r3vIZJMkcvlGn1KlNprGtzqoBqfgFMul4nFYlSr1YlVAlPt7Rzz1ndT9W0Y0AHVcLRI1Q+o+sGk0K53oVR9O9G9UgsstbC7xA0E14Ab2B3Ge3vWMnD00eRzIyx9/UX6RQqqJWlwq4POWjuxrOv4BBhrLdFolK6FxzL7/IvCoA5b1X69b/vF/m1Lxavvr4XH1cJRJl4Y3vXukqAe4sbimvrsyhPOfiWBRHnpG95INBrV75xULUmDWx1U46GdTqfxPI9UKjXxJQqVSgUn00bPosW4OPVWd1DvGin7AeWJEPfrFysnntdb49WgPoa7ZixVvz7ZxjUBtbC1bcSha9YsCoU8J519NkEQUCqVGn1KlNprenFSHVTjy7pu27aNnp4ehoeHaWtrw/M8Ojs7CYKAY978Tp6995ds+NUKBJlYkxvA2vq4bwDfvjg00LP1dUq8cP1tL+w+8YzFCww2GmfR2a/ioRW/5MsP3Ec8mcRaS0dHRwPPhlL7Rlvc6qAavzjZ1tZGrVYjk8lMTMipVqu4rosjwvEXvZEglqQShH3bXkDFe7F1XZ7c5x1Yqr6tt7bDbpPJwwR9HOa85BQ8hFe88Q0EsTi+7+P7PsVisdGnRKm9ttvgFpGbRGSbiKyatO2TIrJJRB4NbxdO2vcPIrJWRJ4WkddMV+GqdUUiEYIgIBaL4XnexOzJaDQ68R2Qc895DenjTqTqW8q+pewbypMvTIbbx/u/a169v7s2cdHyxX7v/oXHkO7qZv3qJzjpVa8i09aGEy5mFY3qPzpV69mTFvc3gQt2sv16a+3i8PYTABE5AbgMODF8zVdERFeoVxPGv3PSdd0dvnvSWjsRplCfFv/aa76A09UzKbCDMMAtpfCiZNV7McwrAVTC0K4GASYao2P2PKJt7Yzlclz6wQ9w7JIlRCKRiTr04qRqRbsNbmvtr4A9Hex6MXCrtbZmrV0HrAWW7Ed96hDzh10l6XQaYwyO41CpVPA8D4B4PM4RC4/msq/cRPvcI6l4JrzVu0hq4+O7x2dTBmZiJErNt9R8i2uFquuRz41wyqvP49XvehfJVIpCoUAQBHpxUrWs/enjvlpEVoZdKV3htlnAC5OO2Rhu+yMicqWIPCwiD3teZT/KUK1kfObk6OgoyWSSfD4PgO/7ZDIZEokE1lqq1SqFQoGFS87idZ++jlMufRM1KxOjTNxIlPmveOXEEMGqH5Ds7adtxhFUg6A+Hb7mEU+n+bP3v5/zrrgCEaFardLZ2UkkEiEajdLe3t7gM6LU3tvXDr6vAtdQ/8rWa4AvAlfszRtYa5cBywDa2wdsrbaPlaiWE4/H6e/vJxKJ0NfXN7E633g3STQaJZ1OT2w77bwLWLT05bz+7z8KhN/y7gjpzk6Kk2Y+RuMJENlhje14Mkn/3LmYcMhhKpVCRCYm3ujKgKoV7VNwW2u3jj8Wka8Dd4VPNwFzJh06O9ym1ITJfdnj95NF/uCLex3HIdbVRVtX1x8d2zUwY48+c/wdxz9PA1u1sn3qKhGRmZOe/hkwPuLkTuAyEUmIyHzgaOC3+1eiUkqpyWR8MsOUB4h8D3gl0AtsBT4RPl9MvatkPfAea+1gePzHqHeb+MBfW2t/ursistlue8wxf7uvf4ZpF4uVOPHEIebNm9foUqa0ZcsWHnssQbX6x63SZtHV9QxLl85v6pEcjz/+OCeddFKjy5iS53msX7+eo48+utGlTCmXy+G6LjNm7Nm/hhph/fr1PNH3BF7Ga3QpU3rmX59hLDe2038a7ja4D4b29n7ruk83uowpdXSs54gj7uOpp97W6FKmNG/ez/jKV/o47bTTGl3KlL70pS/xrne9i2w22+hSpvSxj32Ma6+9ttFlTGl0dJRvfetbfOADH2h0KVN6+OGHGR4e5jWvad5pHLfccgtnn312UzfGjj32WLZt27bT4G6S2QeC6zZvS9HzhgmCRFPXGAQpMpkMXTvpB24WsViMbDbbtDWOr5nSrPVBvcZYLNbUNabTacrlclPXmEgkaGtra+oad3UdRqe8K6VUi9HgVkqpFqPBrZRSLUaDWymlWowGt1JKtRgNbqWUajEa3Eop1WI0uJVSqsVocCulVIvR4FZKqRajwa2UUi1Gg1sppVqMBrdSSrUYDW6llGoxGtxKKdViNLiVUqrFaHArpVSL0eBWSqkWo8GtlFItRoNbKaVajAa3Ukq1GA1upZRqMRrcSinVYjS4lVKqxWhwK6VUi9HgVkqpFqPBrZRSLUaDWymlWowGt1JKtZjdBreIzBGRe0TkCRFZLSIfDLd3i8jdIrImvO8Kt4uI3CAia0VkpYicOt1/CKWUOpzsSYvbBz5krT0BOAu4SkROAD4KrLDWHg2sCJ8D/ClwdHi7EvjqAa9aKaUOY7sNbmvtoLX2d+HjAvAkMAu4GLg5POxm4JLw8cXAt2zdb4BOEZl5wCtXSqnD1F71cYvIkcApwIPAgLV2MNy1BRgIH88CXpj0so3htj98rytF5GERedjzKntZtlJKHb72OLhFpA34EfDX1tr85H3WWgvYvflga+0ya+3p1trTY7HU3rxUKaUOa3sU3CISox7a37HW/jjcvHW8CyS83xZu3wTMmfTy2eE2pZRSB8CejCoR4BvAk9baf520607g8vDx5cAdk7a/MxxdchYwNqlLRSml1H6K7sExLwPeATwuIo+G2/4R+CzwAxF5N7ABeFO47yfAhcBaoAy864BWrJRSh7ndBre19l5Apth97k6Ot8BVe1/KXnWRN0jz11g//c2t2Wts9vpAazxQWqHGnZFmKDyb7bKLF7+90WVMKRJxyWaLxOPdjS5lSr6fp7MzSjqdbnQpU9q2bRs9PT1EIpFGlzKljRs3E40e0egydiHAczYT6481upApmbKhzW+jo6Oj0aVMKZfL0dbWRjweb3QpU/r2t7/NyMjIThvNTRHc7e0Dtljc2ugyppTNruXzn7+Hv/qrv2p0KVO6/fbbGRgY4Mwzz6RWqxGLxTDG1Hc6hi21DYz4W7HGEiUOCBWvTDrSwVEdJyImQjweIwgCRATf9xERHMfB933i8fjE/fj7+75PJBLZ4VgRmXh9LFYPl/plEvjMZzWBPQ4AACAASURBVD7DVVddRVdXV4PO0q5Za3nTmz7Af/7nvze6lCklEjkW/fP5PPKPjzS6lCnNuG8GNw7dyMUXX9zoUqb0ta99jXPPPZeFCxc2upQpDQwMsHXr1p0G9570casWEgQBw8PDJNvj/HbkLvqT8/CdKs8WH2PQ3UChWqRQHeOI1FFU3Ar9sdmsST7JuuG1XH3mx3BrHiJCsVhEREgkEhSLRXp7eykWi3R3dzM2NkZ3dzf5fJ5MJsPo6CixWIx4PE48HicajVIsFps2oJVqdRrch5i1o4/xo5HrkTFhS20DMZvE9y0ZuuhNzKKTLkbLJSrGozsxG0yMnz77Y1LRdq75nw9z2aJ3c0R6Du3t7Vhr8X2fnp4eSqUSiUSCoaEh2trayOfzpFIparUanZ2dWGsJgoByuQxAPB5neHiYzs5OolH930ypA0l/ow4xfel53Lri93Qnu3lJ30tY0H8cz21ez833fo+Fx2Tpy7SxZuUgkVk+LzvhbCJ+klS0k1xhiES6nZt++1Vee/wlnNh1MtFojFgsxvbt2+nv76dUKtHd00NueJhsNsvY2BiZTIZ8Pk8sVj82k8ngOA6lUomuri4cRxegVOpA0+A+xKRIs+y1N/Hh//57/t8TP+Xnq35BwsQZ6JqBuz1BrdDL0f3z2Dy6jmDU8MCjDzB7UTdrt2xmYY/LaHmMai3gqD85js5oChGhra0N13WpFQZ55qk7KeQLdPcfQe+CcwmCgGQyOdGP7bouAI7jUK1WSaVSE/uUUgeGNocOMY7jcEz3Qj5+zsdwosKzw88yUhmhLZmh7JYpeyXm9M/h+N7FdFQWcmTHCRSesYhriFDj+W2b+fnjK7j2rs8A9Qt2xhiwAZue+Dm/vPWveeQnH+eR//4iEl7XNsZgjJkYWuU4Dtbalh1qpVSz0+A+xMRiMTzXY+nspfzorT+it60HJxJhtDpGLB6lFrg8sXE12wvbefr5p/j1ww8wL72IiwbewWMrnuaM4+aQLkT44U9/iOd7ABTyo2zb8BC/+n//zmg5wRlv/AbnXfEdvKA+qsR13YkRLOMXKY0x2tpWappoV8khZmxsbKI/+vgZJ3DfB+7l0v94I4PDgyRsnLhNkCTB9uHtWNcw0DWDwAZs3TbERae+mdEnR8kmRqllUzz7wjMcN/9E/ve2L/DUI3cxZ/7xvPzVV7JoyevI5/O0pdNUq1W6u7sJggDP8ygWi1hrSafTDA0N0dPToxcnlTrA9DfqEDN+sTAajVKtVhlIz+Cmt9zEfz3+X3z1f77K5twguJb2aDsnzDqBuMTZNrqNdDRFIV9AAmgfO5JCxyifuuOv+fOj3szaJ1fSOeMEXv/uL9EzMI9qtUo6ncZ1XWKxGOVyeWL8dipVX+kxCALa29v14qRS00CD+xAzfkHQ87yJSTjH9h3DMa/6G5bMOoOtpa38y3/+C5uGNvPc1mfpTvYQJ87w0BC1ske1WOF9l7yP97/0asbSG/nm9f+Hrm0BH7rm63T1zaFcLpNKpahWqyQSiYlJOeP93OMXJ8cDPZFINPiMKHXo0eA+xBhjiEajuK67w0VCa2HpgqUkU0kuOOECYvEYxUKReETY9Nwz9GV7qFlId/eRjCfp6uwinx/h6fmP8qorXsuRRy9GRAiCAMdxKA5tx4tG8AJDzxGzcBxnIryBiWP1AqVSB54G9yEmmUxOjKuu1WoAE2uDJBIJXNelPdnO0MP3k/QqFLZtpX3zBvKjI3SedAodi8+iuH4t6yoVXtiyjcd/fR9nnfpyvE3Ps3nNUyRTKfJtXWz49QqeX/UYbX0zSS84hraeXmadeCIDRx87MQ0+m81qV4lS00CD+xBTKpXo6emhWCySTCYxxlCr1RARKpUKyUqBdd+5kUxXD24qTbZvBh0v/ROsCAJUNm7AjuVIGJ/Mumd4aa2MXXEXmzetR5woI55Lqn8Wx5x7AUed+xpsYHj6vl+xZdVjPP/7RyhUqlzyj/9EV28vY2Nj9PT0aHgrdYBpcB9iOjo66muVJJOUy2UcxyEWi2GtJROL8Oj7/4rsgqPpOvt8nEgUbIC76fn6wr3WEolEyS48DmMtmTlHsfDSywgCQ62cJ5pqI7AGz/OpjOUwFgJjmb3oZGZay9jwMHf+27/yjf/vPVz9zW/T2dnZ1CsBKtWqtCl0iMnn8/T29k4MyYvFYnieR3VkmAf/8hLSR8xi5p++AVMYw4zlsIUxpFpEKkWolrClPEFuO35uO6ZUwB8bJiiMIK6LO5rDGxnBL+TxSyX8cgmvXMItFqgV690zF//1hyhuGeT//sU7eeHZZwmCoNGnRKlDjra4DzHJZJJSqYSI4Hke1loikQiD//UDuuccxRGvuQhvaJBIOHzPkfBbMkQQazHWghUEC8ZgLQTW4hsIjMFYi7GEzy2BsXjWEliDbwRjLC+97K3cvfwmVt/zP8w/9thGnxKlDjka3IeYdDrN4OAg2WyWSqVCPB7H8WoUnlnJwPGL8Ye24DhSD2oHnDC8qUc11hiwEoZ2OCIlqE99rwe1wRjwjCEw4FtLED73rSWwFgc48qSTefCOO3jFG95I94wZjT0pSh1iNLgPMWNjYwwMDFCpVGhra8MYw6a774Saiwk8gkoJcRwQkEg9tCNO/cJkYKm3qA1YAzYwGFNvhQc2wAQStr4tfmDwDfjG4FnwgoDAgmfqj2csXMiGNWsojoxocCt1gGlwH2Ky2Sxbt26lvb2dUqlEJBIhnYhRiEcwbhXjg3UccMA6Ao7gRBxE6mEtxoKxWGMxQYCZ6BIJW9hBvWvENRY/sPXgDlvcXvjcNWG3ie+BjuNW6oDT4D7EVCoV2tvbASZmLVarVUytiqmUCByIOBGMAyYiGMfBOIKDYGwY2MYQGIsJXuwe8Y0NW9NmosXtGXADE4a1xQvAMzYMcUPgeY08FUodsjS4DzGRSGTi22mCICASiRCNxCiseZJUexZJpfAjDhKpt7rFEZAIAhjqoVu/8BjgBbZ+MxbPGjwf3CDAt/XAdgPYtmEd6f4ZeE4EL6DeEjfg+vVFp5RSB54G9yFmfNy0iEyspZ3o7YNYnPyTjyNHHY1NJLCOg40IVixuqYAk0hCLEfg+nutTq5YZfWo1ru9T9S01Y6n6AdXAUAug/ehFBPE4sXSaaqmML4IXWGpBvctk8/MbGNu+HdFx3IclXc53emlwH2LGl3UtFApkMhl834eXLKFn6Tls/el/ElRKdB55FEE6TeAIEbEEWzch0QTE47iFMWpD23CDej92LTD4gcX1LV4Q4PsWLzBsWvkQNR+ivQPUPB8ybRBP4lphdCjHhjVreOUVf0X3zJmNPiWqAXSNmumlwX2ISafTjI2NEYlEqFarQL0VXqm5+MZSK5cobN1Muq+fymiOiDVQLYNbw1C/EGlsGNgGvMDihhcdfVMfURLYFy9YljZvohZYKoEh0dNHqeYyvHU7xsCCk15Cqq2tsSdEqUOQBvchxnVd2traJsZwB0FAEASkZs3Cj8TA95BCARuPY4e3E7EGEac+4x0IbP3CpDfeV20sbjhixDPgWROOLAkn4VhLQP0iZq1apVKsYERItHVQrdUwxuhaJUodYPobdQga/2fq5H+uLnj7/4fTO4NyEFAuVymNjVHxAiqeoeIZyr6h7AWUfUPFt9R8qPmGmm9wfcJRI/XRIp6xBP6LrXA3MBiEUr5EpVLB9w0nv/YCzn7bWxt1CpQ6pGmL+xATj8epVCo4jlPv3+bFL+91Ovvwn1+HtQFBsYwTGCJi63Mmxy9mUp+EE4xPrglb3rUwtF1Tv1DphRNvXBMeCwTUu1COe9nZRHBIJ1Pa2lZqGuhv1SGmWq3S0dEB1NctiUaj9XHZQcCR73wftUCo+oZK1a23tv3w5gVUfVMfOeKF94GlFliqgcH1DbXw3vctbtj/7Zv6kEHX86lWq0SSCZxEjAuufA/5fF4XmVJqGmiL+xDT3t7O0NAQyWSSYrGIiBCLxYhEIsw/82U8mG7DLYzhCEQdwTGCiB1f1fXFae/UW9zj65G4YUDXx2qDawJqAXhB/Tg3sNhojJf++WU8/ftHmbdoEZlMRr8oWKlpsNsWt4jMEZF7ROQJEVktIh8Mt39SRDaJyKPh7cJJr/kHEVkrIk+LyGum8w+gdlQsFslms1hrSSaTxGIxgiDAGEPZ8zjn35ZPjMcuB/W+7YpnKIf93JUgoOIHk1rghqoX4PpBfdJNOETQ9centwfUDPiB4biXvpxH7rmHq7+2jHg8TrFYnPgqM6XUgbMnzSEf+JC19nci0g48IiJ3h/uut9Z+YfLBInICcBlwInAE8AsROcZaq/9mPgji8TjVanWH73wc72eOx+Mk+geY8bJzeP7XK3DCpV2Fej+3xcFiJ5ZyDcKlXP1wYan6miR2Yoigawy1oN7fnejIUqm6nHnhhcyYN48gCIjFYjoRQ6lpsNsWt7V20Fr7u/BxAXgSmLWLl1wM3GqtrVlr1wFrgSUHoli1e8lkkkKhgIjgui7GGCKRSH2xqXSaaGc3Ryx5KTXfhqNK6i3rim/r9+Eok4pvqAX1fu5qQHirt7ZrQf0CZb2rxGAkyonnvJqK6/LSiy6hvaODIAjIZDIa3EpNg726OCkiRwKnAA+Gm64WkZUicpOIdIXbZgEvTHrZRnYd9OoAyufz9PX1YYypB3U0iud5eJ7HyMgImXSaEy+7nNmvOp+KqXeFlLyAkhtQDocHlsOuklIY4FUvoOr71LyA2viFS9/gBoYgEuPYl/8JuaFhTn31ecxatIjR0VFisRhDQ0N6cVKpabDHwS0ibcCPgL+21uaBrwJHAYuBQeCLe/PBInKliDwsIg97XmVvXqp2oaOjg1wuh+M4lMtlPM8jFosRi8Xo7OykXC4TicWYe96F+LHUxLjtSmDrY7mD8LlvXxxx4huqvqUaWCrjfdzGQjJJ/1ELsdEI5fwYs447jo5sls7OTjzPo7u7W79zUqlpsEeX/EUkRj20v2Ot/TGAtXbrpP1fB+4Kn24C5kx6+exw2w6stcuAZQDt7QO2VtuX8tUfKpfLdIRdFePf8j4+ntt1XZLJJEEQsOTP/pxKbpi7PvlxduzNeHE8d336OxNT3H0bToM3BisR2jq6IJ5gcN16rvz85znxFa+gUqkgIkSjUQqFAh0dHRreSh1gezKqRIBvAE9aa/910vbJqwf9GbAqfHwncJmIJERkPnA08NsDV7LalVQqRT6fx1pLtVrF930cx8FxHDKZDNVqFWst+XyeP7niPZz/8U/iR2L11nQ4nrviG1yJUJm0rRoYXOtQ9QNqvqWGUK5U2bL+ed7xiU9x9Jln1lciTCRIJpP4vq993EpNkz1pcb8MeAfwuIg8Gm77R+AtIrKY+hIX64H3AFhrV4vID4AnqI9IuUpHlBw8kUiEaDRKNBqdmPI+/njyvmg0SjyRYOnb/oKFp53F3V/9v+SHtgP1H+jSt76NX3/n21gLxliiqTRzTjqJJx94AGPBInTPnMHb/vEf6Z4zh2gsNvG+458ZjUY1uJWaBrsNbmvtvYRfBP4HfrKL11wLXLsfdal95DgOvb29U+7PZrMAZDIZAPr7++nv7+fEs8/+o2PPf9df7nMdsVhsn1+rlNo1nfKulFItpknmI1sSiVyji5hSPJ6nWq2SyzVvjeVymWKx2NQ1ep7H6Ohoky+yHzT1/4uJxCgRL0Iil2h0KVOKF+OUy+Wm/n+xWq2Sz+ebusZd/Z5IM/wSdXd327/7u79rdBlTKpVKbN++nSOPPLLRpUxpcHCQRCJBd3d3o0uZ0tNPP82CBQuauhvlscce4+STT250GVPyPI97732OkZFjG13KlJLJHKecUmNmE3/70bp16+jv75/oMmxGX/jCF8jlcju/SGStbfitv7/fNrM1a9bYZcuWNbqMXbrtttvs/fff3+gydumaa66xuVyu0WVMyRhjr7766kaXsUvDw8P2tNOutfUlwZrzNmPGvfb2229v9KnapRtvvNGuWbOm0WXsUpiLO81M7eNWSqkWo8GtlFItRoNbKaVajAa3Ukq1GA1upZRqMRrcSinVYjS4lVKqxWhwK6VUi9HgVkqpFqPBrZRSLUaDWymlWowGt1JKtRgNbqWUajEa3Eop1WI0uJVSqsVocCulVIvR4FZKqRajwa2UUi1Gg1sppVqMBrdSSrUYDW6llGoxGtxKKdViNLiVUqrFaHArpVSL0eBWSqkWo8GtlFItZrfBLSJJEfmtiDwmIqtF5FPh9vki8qCIrBWR74tIPNyeCJ+vDfcfOb1/BKWUOrzsSYu7BpxjrT0ZWAxcICJnAf8HuN5auxAYAd4dHv9uYCTcfn14nFJKqQNkt8Ft64rh01h4s8A5wH+G228GLgkfXxw+J9x/rojIAatYKaUOc3vUxy0iERF5FNgG3A08C4xaa/3wkI3ArPDxLOAFgHD/GNBzIItWSqnD2R4Ft7U2sNYuBmYDS4Dj9veDReRKEXlYRB6uVCr7+3ZKKXXY2KtRJdbaUeAeYCnQKSLRcNdsYFP4eBMwByDcnwWGd/Jey6y1p1trT0+lUvtYvlJKHX72ZFRJn4h0ho9TwHnAk9QD/I3hYZcDd4SP7wyfE+7/H2utPZBFK6XU4Sy6+0OYCdwsIhHqQf8Da+1dIvIEcKuIfAb4PfCN8PhvALeIyFogB1w2DXUrpdRha7fBba1dCZyyk+3PUe/v/sPtVeDPD0h1Siml/ojOnFRKqRajwa2UUi1Gg1sppVrMnlycnHbGGO67775GlzGlLVu2MDg42NQ1rl+/npGREYwxjS5lSrlcjoceeohMJtPoUqZULpeb+udcLBZJJnPMmNG8NXZ1Pc369YWmPo+Dg4OsXLmSrVu3NrqUKe3qd7kpgttay/DwHw31bhpjY2NUKpWmrrFUKrF8uUOh0Lw1zp3rcuaZI1Sr1UaXMqWREZ93vKN5z2E0WmbmBQ+R+vCPG13KlOLrOiiV3tTUvy/VapWPj36carR5/1+s2dqU+5oiuCORCBdddFGjy5jS2rVrCYKgqWs0xrBt2wBbtixtdClT6ulZyfnnn09XV1ejS9kpay233HI369Y17885kcjRMeMLrLtoXaNLmdKM+2Zw4tCJTf37Mjg4yOazNzO2cKzRpUypLdI25T7t41ZKqRajwa2UUi1Gg1sppVqMBrdSSrUYDW6llGoxGtxKKdViNLiVUqrFaHArpVSL0eBWSqkWo8GtlFItRoNbKaVajAa3Ukq1GA1upZRqMRrcSinVYjS4lVKqxWhwK6VUi9HgVkqpFqPBrZRSLUaDWymlWowGt1JKtRgNbqWUajEa3Eop1WI0uJVSqsVocCulVIvZbXCLSFJEfisij4nIahH5VLj9myKyTkQeDW+Lw+0iIjeIyFoRWSkip073H0IppQ4n0T04pgacY60tikgMuFdEfhru+3tr7X/+wfF/Chwd3s4EvhreK6WUOgB22+K2dcXwaSy82V285GLgW+HrfgN0isjM/S9VKaUU7GEft4hERORRYBtwt7X2wXDXtWF3yPUikgi3zQJemPTyjeE2pZRSB8AeBbe1NrDWLgZmA0tEZBHwD8BxwBlAN/CRvflgEblSRB4WkYcrlcpelq2UUoevvRpVYq0dBe4BLrDWDobdITVgObAkPGwTMGfSy2aH2/7wvZZZa0+31p6eSqX2rXqllDoM7cmokj4R6Qwfp4DzgKfG+61FRIBLgFXhS+4E3hmOLjkLGLPWDk5L9UopdRjak1ElM4GbRSRCPeh/YK29S0T+R0T6AAEeBd4bHv8T4EJgLVAG3nXgy1ZKqcPXboPbWrsSOGUn28+Z4ngLXLX/pSmllNoZnTmplFItRoNbKaVajAa3Ukq1GA1upZRqMRrcSinVYvZkOOC0832fr33ta40uY0pjY2Ns3LixqWt87rnnmDs3TW/vykaXMqWOjvXccsstJBKJ3R/cIL6fY9Gi5v05RyJVsuuyLPraokaXMqX0YJoHqg+wZcuWRpcypVWrVnHU2FG4WbfRpUzpef/5Kfc1RXBHIhHOPffcRpcxpY0bN+I4TlPXGI1GOeusbk466aRGlzKlb3xjPddc8wo8r73RpUzpvPN+x223Ne/POZ/P86MfbeNd5+58eoTFYjFYaxFkYhuAI5GJbdNp5cqVjI6OcvbZZ0/7Z+2rsbExvrjki8yePbvRpUxpqbN0yn1NEdwiwsKFCxtdxi6tWbOmqWtctWoVAwMDTV1jJpOhUDiSWq2r0aVMweI48aY+h7lcjkwmw/z58xkeHq5vTHnkS6Nks508tu0e7ivfRaE6gvGFjNNNqVaiXCvx7gWfIhlLMbNtNl2ZHsbGxojFYhSLRXp7exkaGqKjo4NyuUxvby+lUolIJILneQRBQCQSoVQqTezLZrNs376d3t5eAByn3vO6detWIpFIU5/HbDbL7NmzmTNnDsVikVQqRalUIhaLEY1GqVQqtLe3T+yr1WqICLFYjHK5TEdHB4VCgVQqhed5JBIJ6lNYIB6PUywWaWtro1QqkU6n8X0fYwyJRIJCoUB7ezvlcplkMokxBt/3iUajJJNJ6pPRXzyfO9MUwa2U2jsVv8jjlV9S9MfYmF/NcHULyVw7YqL0O/OZlTqJJ4YeIhppZ1H7Ypy2CI/lHuCutd/nNfP+nHPnvY6B5CystSSTSWq12kSIjIeTMWYijMZDZPxYEaFcLhOPxyfu4/F4I0/JPikWi2SzWYrFIl1dXfi+j+d5dHd3MzIyQldX10QIW2up1Wr09vYyMjJCd3c35XKZdDpNpVJBRDDGTLzn8PAw2WyWsbExotEojuOQy+Xo7OxkeHiYjo4O8vk8IkIikaBSqZBIJCaCe1c0uJVqQY443PDbL+MFNWZ3zGZB1wISkQzf/J9b6GiPc8y8mQxvKDFcW83Ji0bpjvfjBYaZqaNYvWUl+FH6EgO85piLACZCZ/yx4zgYY3AcB9/3d/hsEZk4Buqhvidh04xSqRTFYpFoNEo+nycSieA4DmNjY7z//e/n9NNP5z3veQ/lcnnizzw6OkoymSSfzxONRqlWq0Sj9Sh1HGfiL7dsNovrumQyGYwx3HzzzaxYsYKvfe1rZLNZPM+b2Get3ePQBg1upVpSIpLmM2d8hUu+fzHb4gFroznSkqZb5pGuJiivb2NoU4WntmwjkX6c5HA3I91DZKLdRJ04Y/kqVdflrNlnE7UxMpkMpVIJEan/0z9mcaslYtEISBJjLZFIhFqtRiaTwfd9YrEYpVKJ9vb2lg3uUqlEV1cX+XyetrY2giDA8zw6Ojr4yU9+wh133EEQBLzzne+ks7OTWq1GR0fHRIu7WCwSj8epVqsAEy3uzs5ORkdHyWazbNq0iRUrVvCRj3yEWq3G8uXLGR0dpaOjg2Kx/h0142GfSqW0xa3UoaparbKg70h+8KYf8JYfvplH1j9CzI/SE+/GumBcw3Vv+Sy/efwB5nbM5eerf86sOV2sf347ifY2BrcPU3V9rrv7X/jE6z5FqVSio6ODWq1GzFb59j+dhvGrIJZL//73pDpnYIyh8/9v79zD5KqqRP/b59Srux5d/cibQAJpJciVVxInQBhINBDlOYPDQ5GryPgKdxQYAp9fAJ07d3iYBMVHZABhYBCUUQGZUVBUvntnBEMCJBEijSTk2d3pR3VXnao6j73vH+eR6pBHJ2NSXbh/31dfnbPP6Torq1LrrLP22mvl85RKJWKxGIVCgebmZgYGBmhubqa5ubneajlg4vE4rutimiae5/mTusETBUC5XGbJkiUsXbqUZ555hpNOOimKR7uui2EYKKWip44w7KGUIpFI8Oqrr3LOOedQKBQAP4nANM0orBSPx4FdTzna49Zo3sU0NzfT29vLlPRkvvNXK7nmB9fQM9DDjPZOTGUibY8f/r/HSJtpyhWLRCxO94sxjj1qFtt63mSovYcOZyrf//ljLJx2Dh/+wIfp7e0llYCXfv51CkWH8UfOovPEDyLizVSrVUzTpL+/P5qcbGtro7e3l/b29ob1uGOxGI7jYBgGjuNE/477778/8qIBbNvm8ssv54orruCiiy5i2rRp3H777Sil8DwvMsDxeJyrr76a7u5uHnnkER599NHIaAN4nsc999zD1VdfjZSSWCwWzSOYpjl6uf8U/3iNRnN4sSyLTCYDwKzULL5/xSNc8M8X8nrPBrKxLE2iiaqo0lvdyY7e7fTv7Ocjs8+lIzEZicn7M7N45pX/oC0ZI2nEGR4eptDTxVNP3kXPplWMn3Iy8/5mGfnx0zCEwDRNpJS0t7dHHndfXx/ZbLahPe5yuUxbWxtDQ0Pkcjlc18W2bR555BFse2SO97Zt27j99tt5+umnSafTrFq1Cs/zRpxjGAZPP/00SinWrFnzjusppbjnnnu49NJLyefzFItFhBCkUils2448/v2hV05qNA1I6J0ppTCEwYy2Tn752V8yY+J7GKoMsWHHH1i1aTWvbn6VbCbH7PfNpuyUebt7EyJmMLTV5sxjFpFpjrH04cW8ta2Lt7vW8fral5h3/k389eKHaJ94NAL/MT40KGFaoBCCWCyGlBLTNN/hLTaKBx7eeJLJJP39/ViWBYDjONE5y5cvH7GGY926dbzwwgvvMNrgx7hXr149wmhPmDCBBx98MNqPxWKMGzcOx3FoaWkhnU4D/lOUDpVoNO9iDMOgUqkgAm/YcRwmtkzkZ5/5KU+vfZqfrv13/mv9f7KjrxvLLtEnTaqmjbQluPDaht+zcPbZnNFxMePnCq5Zfhnv7TU5cdYC3nPKIpozLZGRDrMehBDYtk08HsfzPBKJRDRJubvBCR//xzphGuDQ0BBtbW2Rxx2GPsA34j/+8Y9pbW3do7HeHwsWLBhxI3Bdl507d5LP5ykUCpHHrdMBNZp3OZVKJQpNlMtl0uk0g4ODZLNZ5s9YwF/Pvpifrf4ZO4Z3YFdssqkM9DO91QAAGQdJREFUZatMtWyDErhnuRw5YSrz58ynrbWN3I42Nv/nK3zor75Ax/jJ9PX1kU6ncRyHWCwWGekwPzmVSjE4OBgt3Mlmsw2Zxx2mA8bjfrgonCCsNdBNTU0cbEPzT33qU9xxxx0888wz0ZhpmuRyuRHpgOAv3NEet0bzLqa5uZmhoSHA/8GHq/HCmG2pVOLsk86mMDhIcyJBebCPtx/8JpWu10hNmsKxX/oH7HgcE9i5Yzs71mwjmR7P1CNnMNTfT2s2i+04dD31I1764UOIeIpjz/8bjjlzPq3t7XieR0dHB8Vikfb29iiPudGoVqtkMhksy6KpqSlaxZhKpaJzbNsmmUxGmScHwgUXXAAwYqJTKUWpVCKdTkfjiURihFe+PxpT2xrNnzmlUilazVcul8lkMlHecPjeveYFxJa32Pj0D4g3pXn/V1aAEUeYBt7OHby29EY8YSArEvnaWsa//2Q2Pv4Am5//FdbwEJmp03nvhZdx3leXIV2H3z/3LA9/8jISLa3M/1/Xkpk4maM6OykUCjQ1NUWTpY1EbfxeKRWFeH7yk58wceJEhoeH2bRpE6tXr37HQqTR0NXVxSmnnEJXV1d0vYsuuiiaE6hNPTyQeQFtuDWaBiSZTI6Icdu2TSqVwnEcUqkUO5//OZuWLWXqpZ/mfTf8H4SA0obXCG2DEoLjly5HCajs2E7rb/8vtm1jCoNZi2+AWJxq2cIuW1h9PUilOOqU2Rx5yhwK/f38281fJjf1SK782l005XIN63HH43Gq1SqGYURL+YUQIzzku+++m7vvvvugPv+6665j27ZtLFu2DPDnJr74xS+STCaRUpJIJKKbxYHoUGeVaDQNSJjNUbsAREqJEILeX/+MN+66lWmXf4bc0e+hunUj1S2bEJUSolKCSgnKJcpvvo71xmu4w4OMnzOXyaf/JS1HTqfcu4PS1s1U+nbilkq4ZQvHsqgOF6kMFTBNk7+84hMMbd7MvZ//XJTG1oiEaZVhvDk0pMuWLTvouPbuhEYb/O9t6dKlFAq+HovFIuVyOaqDMlo9NuZtUqP5MyfM6hBCRCv5LMtC9HXT/ZOHOfLCj5Fs60AW+jAwECJYEQgIQKJA+ttIhW0V8ZTCleBJhVQKqfxtN3yXCg+J40Ei2cTpl3+cJ76+gm9+6pNc/8j366uQgyRcvp5KpRgYGEApxbe+9S2+9rWvjQiNtLa2YprmiLTIgYGBPX5mS0sL8Xg8upFKKaNzlVLce++9mKbJLbfcEmWqeJ53QOmA2uPWaBqQMKYdVp4rFArkW1rYsXYNuY6JpPPtyOIgVCxEtYhRtTCrJYyq5b9C77tcgkoRyiWkVUJZRTyriGsVcUvD2KUiTnEYuziMXRqmOuy/V4pDSNfhQ1d9moEtWxju6am3Sg6K4eFh8vk8tm2TzWb57ne/y1e/+tURi2+OO+44Vq9ezZYtW3jzzTfp6elh1apVzJ49+x2fN3PmTJ577jm2bNnC2rVr2bJlCy+++CInnHBCdI7neXz729/mjjvuYNu2bZRKJcD3/kfrcWvDrdE0IGFBomQyied5flpbYZDB3/wMoymFMzwAFQtVtqDiG2qjahGrljCrFqJiQdWKzvGsEqpsIcslZNlCWhauZeFaRRyrhB2+l0rYpSJ2qUi1VMSp2MTTGX79aGN63E1NTViWRSwWo7u7m5tvvnnE8fe9732sXLmStra2KBY+NDTEuHHjWLZsGZ2dndG5yWSS66+/ns7OTqrVKtlsFsdxmDBhAvfddx9z5swZ8dnLli2jVCpFHaF0OqBG8y4nDI2A/4O3bZukIaj88fe0LzgXWS7hGQamIXz3zADTMDEMkAqEVCAVSiqUlChPISV4UiIluFLhSIWjJI7nh1BcKf0xqXC9YFvBxGlH4fyJ4sGHG8dxaG5uplKp8NnPfjbKLgnZvn07N9xwA57nceyxx/LNb36TVCqFZVmcdNJJLFy4kDfeeAOAhQsXctZZZ2HbdnRDuPXWW1mzZg1SSjZt2jTi2kIIvvCFL/CjH/2IRCJxQKmG2nBrNA1IbfpalNJmCJT0kBUL1wDDMJGGQBkCDIEyBYSGSYKSCikl0vPfXQmuJ3EVOK7EVX5c2/akb8g9iSslthQ4nsKREseTVErFeqvjoAkbGMRiMe677z5+85vfcPnll0fH+/v7+e1vf8sxxxzDbbfdhmmaWJZFMpmkWq2OyATJZrOMGzcuyvJJp9PcfPPNLFq0iNWrV7/j2t/4xje47LLLRjSwGC3acGs0DYht29FKRc/zSKVSVAqDeCWLSvc2mnIteIaJYQqEAcIUIAwkBhKFqxSe9A2y64VetcJVEtsDJ/SoPX8yslwuU3UcSDZhSxUYbnCkR9WyaMycEkYUdTJNk+eff/4d58ycOZPHHnuMTCZDLBbj2Wefpaenh3w+zwknnMCVV16J67p84AMf4IUXXmDjxo00NTVx4YUXkkqleOKJJzj33HN55ZVXRnzu7373Oz760Y9GHv6BZOZow63RNCCpVIqenh6EEKTTab8PYjaDVDD0+nrMzmMRTSkwjMDTDjJJHBeRTOEp6Rte16W0bTOVUomKJ7E9RdVVVKVH1YV4+wTI5qhYZaq2jXA97OA8Ryps12PTunXMmD1n/0KPUcJOP8VikZUrV3L++eezYcMGNmzYABClB955550IIejr6+Paa6/l1FNP5fHHH+eiiy6KyrN+5jOf4fHHH2f58uWAX5dk6dKlI4zylClTWLBgAQ8//DBLliyhubl51FUBQ7Th1mgakLBZb7hYJJvNMlwc5rgl/8j6r3wRb22Jjvcej0om8AyBJ0BULeTgAOaEyUjXY7hrPZ6rqFSrVB2HqiepulB2PaqupOJJnB3bcDBR6RbMljzKquCaMRwPbE/StfZVjEQzx50+r94qOSjCxr6pVIpUKsWLL75IR0cHH//4x6NzXn/9dTZs2MDzzz/PJZdcwlVXXUVbW1uU7ud5XtQ8wfM8MpkM5513Hvfffz8rVqxg48aNUT0SgHw+z4oVK7jmmmuYPn161HXoQBbgaMOt0TQonudFfR99r9FEZFtxXIlRKtH/+5dpmXEshudiSg/hVHF6t8L2LX6utgRHSmzpe9C263vRHkHutgK7alNxPCqFYaqbN1PxJG48SXriZLZt3MTwsMW0Oe/h+DPOqLM2Do6wsW+1WqWtrY3W1lY2b95MpVKJFjWB73W/9dZb3Hbbbaxfv54nn3yS733veyilaGpqitIHjz/+eK6//npuvPFGHnvssXeEPwzDoFwus337dmbOnBkt8onH41QqlSjDZH+M2nALIUxgFbBVKXWuEGI68CjQDrwEXKGUsoUQSeBfgFOAPuASpdTG0V5Ho9Hsn3Cpdmi8w/KqRUCmUtjVCjgupcEBKA0hisMYhsBAoFB4SiKVb7hdSRCz3hW7dsP4t/Tj4VIqPKXwJHiOQ3FgkIpVxkymUKpx6m/vTiaTibqxDw4OkkgkePPNNzn11FM5++yzGRoaiiYwV65ciVKKp556irlz57JkyZKo2306nUYpxXXXXcdDDz00wmgvXrw48sjD4mBdXV1MnjyZXC6H53lRJspoORCP+++A14BcsH87sEIp9agQYiVwFfCd4H1AKTVDCHFpcN4lB3AdjUazH6rValTBzrIsmpub/TKrM/8HracvpPvnP0Hiovr6iAmJ4UqEIRCB4ZaqxhAr5ce2PTXCgLs1k5eu8icsPaVwHUV1oIBUYKZSnHfD30c1UhqNMORk2zYtLS0opZg3bx7z58+nUqlEnWkMw6Czs5Nrr70WgLvuuosvfelLUTqhbdvRKsnly5dHRvuWW27hc5/7HKlUKlrlmkqlqFQqUVVHIOoWP9rSuKNagCOEOAL4CHBvsC+A+cDjwSkPAhcG2xcE+wTHF4hGvR1rNGOUdDpNsVgcUUu6paWFqjDJHTUDV0LVkZStMuWyjeVJyq7Ecv33siupuL6xLjvKn5iUEjtI/3OUoioVrqdwlcAOPG5HSox0xg8lJJpwXJe5Hzq7IduWgV8et1aHYchjaGiIpqYmhoaGou72M2fOjP7Odd2ol2SlUiEej49oAhzS2dlJa2sr8XgcwzDI5XKUy2VaWlqi+iihp30g9cxH63HfBdwAZIP9dmBQKRUu5t8CTAm2pwCbAZRSrhCiEJy/c9RSaTSafWJZFtlsdsR2oVAgm81iTOvEGDeZyo4tOMrGRGAaBJUBfV9NqZFed7i4JsoW8TwczzfetgzzuRWuB5WBQaSA9y84i1RbO729veTz+UieRiKs8xLmUYdzBrFYLGoCrJTCNM0Rk4dCiCjvOqxhUvsKCbvBh2OO40R53mGIK4yj105g7o/9etxCiHOBHqXUS6P+1FEghPhbIcQqIcSqP1UVLo3mz4Uw7loul6MJr/Cx/qjTziQ15UjKnqQSZIf4Hrak4rpUXJey61F2vV3HIyMdTFR6ys/nDo15kOftSD+E0jFtOn9ct55zP7+YXC7XkN1vYFcqYGica3O6wwqMYfXF6dOnj2iM8Itf/AIgCpGE8e++vj7Ab1l2/PHHR8fCrBPDMPA8b8TfwZ8+j/s04HwhxIeBFH6M++tAXggRC7zuI4CtwflbganAFiFEDGjBn6QcgVLqHuAegAkTJjRq/r5GUxfCH3744w8zIEKDM+vvv8pTHz+PcrmIKYQ/Mal8r1sBEpBhFUAUrutnkvjGWeJ6YEvfmDtSBtknvgFPZnOMn/Fexs2YQdukSVG7r0YkbBKcy+UoFAokEgni8XjUSai/v59sNotlWeTzeebNm8cTTzxBqVRi8eLFTJ06NTLsAFu2bIkqAZ5yyilMmjQpqpMe1pQZGBiIOsuHrcts2/7TpgMqpW4CbgIQQpwJXK+U+pgQ4ofAxfiZJVcCTwR/8mSw/1/B8edUoxbr1WjGKJ7nRT/08JHesiwSiQTlcpn80cfQfOR0eta/jCEMzKikq0RhoETgAQaTk55UQQnXsB6JiDxtR0oqnh8ysaVHNpfHSCSYfsIJZPN5hoaGMAyjIb3usDpgpVIhn88jpcTzPNra2qK2bOVymWw2i1Iqqg8D0NvbS29v714/O3wKCmtvG4bBwMAA6XSa/v7+KIYehl3CZsGj4b9THXAJcK0Qogs/hn1fMH4f0B6MXwvc+N+4hkaj2QPpdJrh4WGKxSKxWCzKR7Ysi/b2dizLYtG3vkfVkVRdj7LjBeER5b/bkrLjh0+qYRjFU5Q9qLiCiiuxPUnV88cdT2K7Hq1TjqTztHmkmtMsvPRShoeH6ejoaNjJyWw2y8DAAIlEgoGBgSivOmyAvHPnTkzTZGhoCMuymD17NlOnTt3v506cOJGzzjoruiEkk0kMw4j6gXZ0dESZLOl0GuCAdHhAhlsp9Wul1LnB9h+VUnOUUjOUUh9VSlWD8UqwPyM4/scDuYZGo9k/5XKZ5uZmmpqaoiL84QrAQqFAKpVCxRKccMWnfUPt+YbbcnbFtv3sEs+Pf3uqxoj7y9qrrqQaxbsVuYlTOHrWHLZt3MgHP/lJCsNFmpqaGBwcHNHqq5GwLCvquJ7L5aKUxnw+H4VHPM8jnU6TSqU47bTTePDBB8nn83v9zEQiwb333suZZ55JMplkeHgYx3FQSkXZKgMDA37efdABBzggHep63BpNA5JMJnEcJ8pSKJfL0Qq+TCbjNwZobaNj7hkY4yZRdhWWK7E8PyVwV1qg2rXtSSqO53vZrp8iWPU8bKlI5FoYP6OTvp5urOEiR594Itlslmq1SjqdPqDKdmOJVCpFqVQiFotRKpWidMDwJjg8PIxpmlQqlagn5cyZM1mzZg0PPPAAuVyObDZLLpcjl8uxYsUKNmzYwNy5c8lms9i2TXNzM7FYLKorE5YocF2X5ubmEfW4R4te8q7RNCC1S7HDjIja2hnhpOX0OXOZ9YlP89yKO3GsUvT3KliIo5Q/SekRxrvxy7lGC3AkqbYOMhMmYZXLJJMpbn/2mUiG2knRRqS2vVhIbXuy2mNh+VzDMBg/fjyLFi3i7bffxnXdaGUkEM03hPW1pZRR9kjtdwT+/ERt1slo0YZbo2lAPM+LUtVCw+m6LoZh4DhO9J5IJJh31WfxlOKn//srqBEGys8w8RR+Tne4rF3tqsvtKoHhKQoDA0ybNIlP33knRlAJr1qtRjnJQoiG7PRea3TD1Y3ge+JhuVwY6Q2Hx2oXztSm9DmOQzwejzJFHMeJ/ta27ehY+J3V3ihGiw6VaDQNSJizXalUouL+4VjYtTx81DcMgzmXf4KLv/YNjjhpth/PDl5TZs0hNWEiFU8GL0XnGWdSlfhL4CVUrDInf+iDfPKf/onm1laSySRSSjKZDNVqlUwm05AZJUBkWMPFMKHxrDW64VL10AMPK/mFYZUwN1sIgWEYxOPxqJmzlJJYLBYdj8fjuK474lh4wzuQp5bGu0VqNBoA2traAP8RvqmpCSFENNba2ooQgsmTJ0fH53/ifzLvo5fg1XiAZjyOlB7S2+WJxxIJnJpmuQCJVIpEKhV5h7lcDiEE7e3tDZvDDf4NMJlMjtAh7AqXhMdqCbux7+lYyL7i1gcT094dbbg1mgYlXPQBu6rz7e/dzGRG9dmpIEVtd/b2uY1KuIgp3K4d331sNMcOFzpUotFoNA2GGAuLGltbW9UVV1xRbzH2SrVajVZRjVUKhQKxWCxK5h+LdHd3093dgVJjNwMhn9/KUUdN2f+JdcLzPPr6+hg/fny9RdkrpVIJz/PI5XL7P7lO9PX1kclkRr1SsR489NBDDAwM7NGtHxOGWwjRC5QYuxUEO9CyHQxatoNDy3ZwvNtkO0opNW5PB8aE4QYQQqxSSs2qtxx7Qst2cGjZDg4t28Hx5ySbjnFrNBpNg6ENt0aj0TQYY8lw31NvAfaBlu3g0LIdHFq2g+PPRrYxE+PWaDQazegYSx63RqPRaEZB3Q23EOIcIcQGIUSXEKLuTReEEBuFEGuFEC8LIVYFY21CiGeFEG8E762HSZb7hRA9Qoh1NWN7lEX4fCPQ46tCiJPrJN+tQoitgf5eDlrehcduCuTbIIQ4+xDKNVUI8SshxO+FEOuFEH8XjNddd/uQre56C66VEkK8KIR4JZDvK8H4dCHEC4EcjwkhEsF4MtjvCo5Pq4NsDwgh3qrR3YnBeD1+E6YQYo0Q4qfB/qHR2+7diQ/nCzCBN4GjgQTwCnBcnWXaCHTsNnYHcGOwfSNw+2GS5QzgZGDd/mQBPgz8ByCAvwBeqJN8t+K3t9v93OOC7zcJTA++d/MQyTUJODnYzgJ/CK5fd93tQ7a66y24ngAywXYceCHQyQ+AS4PxlcDngu3PAyuD7UuBx+og2wPAxXs4vx6/iWuBR4CfBvuHRG/19rjnAF3K76Zj4/evvKDOMu2JC4AHg+0HgQsPx0WVUs8D/aOU5QLgX5TPb/GbOU+qg3x74wLgUaVUVSn1FtCF//0fCrm2K6VWB9vDwGvAFMaA7vYh2944bHoLZFJKqWKwGw9eCpgPPB6M7667UKePAwuEODRFPPYh2944rL8JIcQRwEeAe4N9wSHSW70N9xRgc83+Fvb9n/hwoIBnhBAvCSH+NhiboJTaHmzvACbUR7R9yjKWdLk4eDS9vyasVBf5gkfQk/C9szGlu91kgzGit+Bx/2WgB3gW38sfVEq5e5Ahki84XsDvQXtYZFNKhbr7x0B3K4QQ4Tr2w627u4AbgLDUYjuHSG/1NtxjkdOVUicDi4AvCCHOqD2o/GebMZGKM5ZkqeE7wDHAicB2YFm9BBFCZIB/A76olBqqPVZv3e1BtjGjN6WUp5Q6ETgC37s/tl6y7M7usgkhjgduwpdxNtCG38j8sCKEOBfoUUq9dDiuV2/DvRWobZl8RDBWN5RSW4P3HuDH+P9xu8NHrOC9p34S7lWWMaFLpVR38OOSwD+z67H+sMonhIjjG8Z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}, "metadata": { "needs_background": "light" @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -289,8 +289,8 @@ "output_type": "display_data", "data": { "text/plain": "
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\n"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\n" }, "metadata": { "needs_background": "light" @@ -383,14 +383,25 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "" + "Epoch = 2" + ] + }, + { + "output_type": "error", + "ename": "IndexError", + "evalue": "index 8 is out of bounds for axis 0 with size 8", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mgamma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mai\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mai\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mai\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgamma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdpos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdpos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: index 8 is out of bounds for axis 0 with size 8" ] } ],