80 lines
1.5 KiB
Plaintext
80 lines
1.5 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 1]\n",
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"[1 0]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"y_out = [[[-0.52742714, -0.8918941 , -0.53989583, -0.874211 ]]]\n",
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"\n",
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"outputs = y_out[0][0]\n",
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"\n",
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"prob_action1 = outputs[:2]\n",
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"prob_action2 = outputs[2:]\n",
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"\n",
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"norm_action1 = [float(i)/sum(prob_action1) for i in prob_action1]\n",
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"norm_action2 = [float(i)/sum(prob_action2) for i in prob_action2]\n",
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"\n",
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"action1 = np.random.multinomial(1,norm_action1)\n",
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"action2 = np.random.multinomial(1,norm_action2)\n",
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"print(action1)\n",
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"print(action2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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