122 lines
55 KiB
Plaintext
122 lines
55 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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"from matplotlib import cm\n",
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"# matplotlib.use(\"agg\")\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.ticker as ticker\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 import SynAdaptiveEnv\n",
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"from xlayers.super_core import SuperSequential, SuperMLPv1"
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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": "stdout",
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"output_type": "stream",
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"text": [
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"SynAdaptiveEnv(100/100 elements,\n",
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"amplitude=QuadraticFunc(y = -12.000007629394531 * x^2 + 11.999908447265625 * x + 0.9999204277992249),\n",
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"period_phase_shift=QuarticFunc(y = 6.985218524932861 * x^4 + -13.632467269897461 * x^3 + -17.948883056640625 * x^2 + 53.29509735107422 * x + 53.29509735107422))\n"
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]
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1440x576 with 1 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_q_func():\n",
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" # save_dir = (save_path / '..').resolve()\n",
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" # save_dir.mkdir(parents=True, exist_ok=True)\n",
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" \n",
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" dpi, width, height = 10, 200, 80\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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" dataset = SynAdaptiveEnv()\n",
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" print(dataset)\n",
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" xaxis, yaxis = [], []\n",
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" for idx, position, value in dataset:\n",
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" xaxis.append(position)\n",
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" # yaxis.append(dataset._amplitude_scale[position])\n",
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" yaxis.append(value)\n",
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"\n",
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" cur_ax = fig.add_subplot(1, 1, 1)\n",
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" cur_ax.plot(xaxis, yaxis, color=\"k\", linestyle=\"-\", alpha=0.6, label=None)\n",
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"\n",
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" # fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
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" # plt.close(\"all\")\n",
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" # plt.show()\n",
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"visualize_q_func()"
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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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