138 lines
365 KiB
Plaintext
138 lines
365 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": 1,
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"id": "filled-multiple",
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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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"The root path: /Users/xuanyidong/Desktop/AutoDL-Projects\n",
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"The library path: /Users/xuanyidong/Desktop/AutoDL-Projects/lib\n"
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]
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}
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],
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"source": [
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"import os, sys\n",
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"import torch\n",
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
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"root_dir = (Path(__file__).parent / \"..\").resolve()\n",
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"lib_dir = (root_dir / \"lib\").resolve()\n",
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"print(\"The root path: {:}\".format(root_dir))\n",
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"print(\"The library path: {:}\".format(lib_dir))\n",
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"assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
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"if str(lib_dir) not in sys.path:\n",
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" sys.path.insert(0, str(lib_dir))\n",
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"\n",
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"from datasets.synthetic_example import create_example_v1"
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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": 2,
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"id": "consistent-transition",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/xuanyidong/Desktop/AutoDL-Projects/lib/datasets/synthetic_env.py:63: RuntimeWarning: covariance is not positive-semidefinite.\n",
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" dataset = np.random.multivariate_normal(\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 5760x2880 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"def visualize_env():\n",
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" \n",
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" dpi, width, height = 10, 800, 400\n",
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" figsize = width / float(dpi), height / float(dpi)\n",
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" LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
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"\n",
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" fig = plt.figure(figsize=figsize)\n",
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"\n",
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" dynamic_env, function = create_example_v1(num_per_task=250)\n",
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" \n",
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" timeaxis, xaxis, yaxis = [], [], []\n",
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" for timestamp, dataset in dynamic_env:\n",
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" num = dataset.shape[0]\n",
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" timeaxis.append(torch.zeros(num) + timestamp)\n",
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" xaxis.append(dataset[:,0])\n",
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" # compute the ground truth\n",
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" function.set_timestamp(timestamp)\n",
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" yaxis.append(function(dataset[:,0]))\n",
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" \n",
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" timeaxis = torch.cat(timeaxis).numpy()\n",
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" # import pdb; pdb.set_trace()\n",
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" xaxis = torch.cat(xaxis).numpy()\n",
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" yaxis = torch.cat(yaxis).numpy()\n",
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"\n",
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" cur_ax = fig.add_subplot(2, 1, 1)\n",
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" cur_ax.scatter(timeaxis, xaxis, color=\"k\", linestyle=\"-\", alpha=0.9, label=None)\n",
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" cur_ax.set_xlabel(\"Time\", fontsize=LabelSize)\n",
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" cur_ax.set_ylabel(\"X\", rotation=0, fontsize=LabelSize)\n",
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" for tick in cur_ax.xaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" tick.label.set_rotation(10)\n",
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" for tick in cur_ax.yaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" \n",
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" cur_ax = fig.add_subplot(2, 1, 2)\n",
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" cur_ax.scatter(timeaxis, yaxis, color=\"k\", linestyle=\"-\", alpha=0.9, label=None)\n",
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" cur_ax.set_xlabel(\"Time\", fontsize=LabelSize)\n",
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" cur_ax.set_ylabel(\"Y\", rotation=0, fontsize=LabelSize)\n",
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" for tick in cur_ax.xaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" tick.label.set_rotation(10)\n",
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" for tick in cur_ax.yaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" plt.show()\n",
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"\n",
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"visualize_env()"
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]
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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": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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