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2415 lines
75 KiB
Plaintext
2415 lines
75 KiB
Plaintext
2 years ago
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# %pip install sklearn"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.preprocessing import MinMaxScaler"
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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": 59,
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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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"(40, 2)\n"
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]
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}
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],
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"source": [
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"# 加载数据集\n",
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"data = pd.read_csv('translated_speed_strain_useOriginSpeed.csv')\n",
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"data=data.head(200)\n",
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"# 提取特征\n",
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"# features = data[['焊接电压', '焊接电流', '焊接速度', 'x', 'y', 'z', '3', '4', '5','strain']]\n",
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"\n",
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"\n",
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"features=pd.DataFrame()\n",
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"features['UIV']=data['焊接电压']*data['焊接电流']/data['焊接速度']\n",
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"features[\"Q\"]=(data['焊接电压']*data['焊接电流']/data['焊接速度'])*0.75\n",
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"mu,sigma=0,5\n",
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"noise= np.random.normal(mu, sigma, len(features[\"Q\"]))\n",
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"features[\"Q\"]=features[\"Q\"]+noise\n",
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"\n",
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"\n",
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"features=features.to_numpy()\n",
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"\n",
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"train_size = int(0.8*len(features))\n",
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"train_sequences = features[:train_size]\n",
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"test_sequences = features[train_size:]\n",
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"\n",
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"\n",
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"print(test_sequences.shape)"
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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": 60,
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"metadata": {},
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"outputs": [],
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"source": [
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"class LSTM(nn.Module):\n",
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" def __init__(self, input_size, hidden_size, num_layers, output_size):\n",
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" super(LSTM, self).__init__()\n",
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" # self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
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" # self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
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" # self.lstm=nn.RNN(input_size, hidden_size, num_layers, batch_first=True)\n",
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" self.fc0=nn.Linear(input_size,hidden_size,bias=False)\n",
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" self.relu = nn.ReLU()\n",
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" self.fc = nn.Linear(hidden_size, output_size)\n",
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" \n",
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" nn.init.constant_(self.fc0.weight,0.78)\n",
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" nn.init.xavier_uniform_(self.fc.weight)\n",
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" nn.init.zeros_(self.fc.bias)\n",
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"\n",
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" def forward(self, x):\n",
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" # out, _ = self.lstm(x)\n",
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" out0=self.fc0(x)\n",
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" # out1=self.relu(out0)\n",
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" # print(f\"out{out[0]}\")\n",
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" # print(f\"out{out[-1]}\")\n",
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" # out2 = self.fc(out1)\n",
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" return out0"
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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": 61,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_size = 1\n",
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"hidden_size = 1\n",
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"num_layers = 2\n",
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"output_size = 1\n",
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"model = LSTM(input_size, hidden_size, num_layers, output_size)\n",
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"\n",
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"# 定义优化器和损失函数\n",
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"learning_rate = 0.01\n",
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"# learning_rate=1\n",
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"criterion = nn.MSELoss()\n",
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"# criterion=nn.L1Loss()\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
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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": 62,
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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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"Epoch 1/50, Loss: 26.1416\n",
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"[[0.7336207032203674]]\n",
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"Epoch 2/50, Loss: 20.0536\n",
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"[[0.7503769993782043]]\n",
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"Epoch 3/50, Loss: 18.3776\n",
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"[[0.7569617033004761]]\n",
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"Epoch 4/50, Loss: 20.1773\n",
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"[[0.7479583024978638]]\n",
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"Epoch 5/50, Loss: 19.8454\n",
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"[[0.7500355839729309]]\n",
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"Epoch 6/50, Loss: 19.2315\n",
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"[[0.7522401809692383]]\n",
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"Epoch 7/50, Loss: 19.7850\n",
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"[[0.7499220967292786]]\n",
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"Epoch 8/50, Loss: 19.5623\n",
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"[[0.7510177493095398]]\n",
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"Epoch 9/50, Loss: 19.5571\n",
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"[[0.7509536743164062]]\n",
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"Epoch 10/50, Loss: 19.6496\n",
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"[[0.7506616711616516]]\n",
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"Epoch 11/50, Loss: 19.5702\n",
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"[[0.7510122060775757]]\n",
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"Epoch 12/50, Loss: 19.6270\n",
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"[[0.7507977485656738]]\n",
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"Epoch 13/50, Loss: 19.6025\n",
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"[[0.7509378790855408]]\n",
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"Epoch 14/50, Loss: 19.6167\n",
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"[[0.7508964538574219]]\n",
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"Epoch 15/50, Loss: 19.6162\n",
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"[[0.7509273290634155]]\n",
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"Epoch 16/50, Loss: 19.6183\n",
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"[[0.7509388327598572]]\n",
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"Epoch 17/50, Loss: 19.6221\n",
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"[[0.7509452700614929]]\n",
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"Epoch 18/50, Loss: 19.6225\n",
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"[[0.7509629726409912]]\n",
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"Epoch 19/50, Loss: 19.6260\n",
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"[[0.7509673833847046]]\n",
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"Epoch 20/50, Loss: 19.6267\n",
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"[[0.7509824633598328]]\n",
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"Epoch 21/50, Loss: 19.6294\n",
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"[[0.7509878873825073]]\n",
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"Epoch 22/50, Loss: 19.6303\n",
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"[[0.7510002255439758]]\n",
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"Epoch 23/50, Loss: 19.6325\n",
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"[[0.7510063052177429]]\n",
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"Epoch 24/50, Loss: 19.6334\n",
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"[[0.7510166168212891]]\n",
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"Epoch 25/50, Loss: 19.6352\n",
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"[[0.7510228157043457]]\n",
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"Epoch 26/50, Loss: 19.6362\n",
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"[[0.7510318756103516]]\n",
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"Epoch 27/50, Loss: 19.6378\n",
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"[[0.7510377168655396]]\n",
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"Epoch 28/50, Loss: 19.6386\n",
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"[[0.7510459423065186]]\n",
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"Epoch 29/50, Loss: 19.6401\n",
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"[[0.7510514259338379]]\n",
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"Epoch 30/50, Loss: 19.6409\n",
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"[[0.7510588765144348]]\n",
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"Epoch 31/50, Loss: 19.6421\n",
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"[[0.7510640621185303]]\n",
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"Epoch 32/50, Loss: 19.6429\n",
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"[[0.7510709166526794]]\n",
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"Epoch 33/50, Loss: 19.6440\n",
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"[[0.7510756850242615]]\n",
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"Epoch 34/50, Loss: 19.6447\n",
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"[[0.7510820627212524]]\n",
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"Epoch 35/50, Loss: 19.6458\n",
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"[[0.7510864734649658]]\n",
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"Epoch 36/50, Loss: 19.6464\n",
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"[[0.7510923743247986]]\n",
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"Epoch 37/50, Loss: 19.6473\n",
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"[[0.7510964870452881]]\n",
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"Epoch 38/50, Loss: 19.6479\n",
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"[[0.7511020302772522]]\n",
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"Epoch 39/50, Loss: 19.6488\n",
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"[[0.751105785369873]]\n",
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"Epoch 40/50, Loss: 19.6493\n",
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"[[0.7511110901832581]]\n",
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"Epoch 41/50, Loss: 19.6502\n",
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"[[0.7511143684387207]]\n",
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"Epoch 42/50, Loss: 19.6505\n",
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"[[0.7511195540428162]]\n",
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"Epoch 43/50, Loss: 19.6515\n",
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"[[0.7511224150657654]]\n",
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"Epoch 44/50, Loss: 19.6517\n",
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"[[0.7511274814605713]]\n",
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"Epoch 45/50, Loss: 19.6527\n",
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"[[0.7511299252510071]]\n",
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"Epoch 46/50, Loss: 19.6528\n",
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"[[0.751134991645813]]\n",
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"Epoch 47/50, Loss: 19.6537\n",
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"[[0.7511370182037354]]\n",
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"Epoch 48/50, Loss: 19.6538\n",
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"[[0.7511419653892517]]\n",
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"Epoch 49/50, Loss: 19.6548\n",
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"[[0.7511436939239502]]\n",
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"Epoch 50/50, Loss: 19.6548\n",
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"[[0.7511485815048218]]\n"
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]
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}
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],
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"source": [
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|
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"# 训练模型\n",
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"num_epochs = 50\n",
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"batch_size = 16\n",
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|
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"for epoch in range(num_epochs):\n",
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" for i in range(0, len(train_sequences), batch_size):\n",
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" batch_sequences = train_sequences[i:i+batch_size]\n",
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|
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" # print(batch_sequences.shape)\n",
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" inputs = torch.tensor(batch_sequences[:, :-1], dtype=torch.float32)\n",
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" \n",
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" targets = torch.tensor(batch_sequences[:,-1], dtype=torch.float32)\n",
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" if len(inputs) != batch_size:\n",
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" continue\n",
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" \n",
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" # 前向传播\n",
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" outputs = model(inputs)\n",
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|
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" # print(f\"targets {targets}\")\n",
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" \n",
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" # print(f\"input:{inputs}\")\n",
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" \n",
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" # print(f\"outputs {outputs}\")\n",
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" \n",
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" outputs=outputs.reshape(targets.shape)\n",
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|
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" # print(outputs.shape)\n",
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" # print(f\"output:{outputs.shape}\")\n",
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" # print(f\"target:{targets.shape}\")\n",
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|
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" loss = criterion(outputs, targets)\n",
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|
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" # print(f\"loss:{loss}\")\n",
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|
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" # loss=loss*1000\n",
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|
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" # 反向传播和优化\n",
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|
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" loss.backward()\n",
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|
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" optimizer.step()\n",
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|
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" optimizer.zero_grad()\n",
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" \n",
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" \n",
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" \n",
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|
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" # if epoch>996:\n",
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" if True:\n",
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|
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" print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')\n",
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" \n",
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|
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" print(model.fc0.weight.tolist())\n",
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|
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" # print(model.fc.weight.tolist())\n",
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|
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" # print(model.fc.bias)\n",
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|
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" # print(model.fc.weight[0][0])\n",
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" \n"
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]
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},
|
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|
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{
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||
|
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"cell_type": "code",
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||
|
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"execution_count": null,
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"metadata": {},
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||
|
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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||
|
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"traceback": [
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||
|
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||
|
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"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
|
||
|
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]
|
||
|
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}
|
||
|
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],
|
||
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"source": [
|
||
|
|
"# num_epochs = 30\n",
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||
|
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"# batch_size = 32\n",
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|
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"# learning_rate=0.05\n",
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|
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"# for k in range(8):\n",
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"# learning_rate*=0.5\n",
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"# print(f\"learning_rate:{learning_rate}\")\n",
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|
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"# for epoch in range(num_epochs):\n",
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|
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"# for i in range(0, len(train_sequences), batch_size):\n",
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|
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"# batch_sequences = train_sequences[i:i+batch_size]\n",
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|
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"# inputs = torch.tensor(batch_sequences[:, :-1,:-1], dtype=torch.float32)\n",
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"# targets = torch.tensor(batch_sequences[:, -1, -1], dtype=torch.float32)\n",
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|
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"# if len(inputs) != batch_size:\n",
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|
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"# continue\n",
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" \n",
|
||
|
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"# # 前向传播\n",
|
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|
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"# # print(inputs.shape)\n",
|
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|
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"# outputs = model(inputs)\n",
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|
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"# loss = criterion(outputs, targets)\n",
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" \n",
|
||
|
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"# # 反向传播和优化\n",
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|
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"# optimizer.zero_grad()\n",
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|
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"# loss.backward()\n",
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|
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"# optimizer.step()\n",
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" \n",
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|
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"# print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')"
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]
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},
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{
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|
|
"cell_type": "code",
|
||
|
|
"execution_count": 54,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"test_inputs_shapetorch.Size([40, 1])\n",
|
||
|
|
"Test Loss: 21.8248\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# 在测试集上进行评估\n",
|
||
|
|
"model.eval()\n",
|
||
|
|
"test_inputs = torch.tensor(test_sequences[ :,:-1], dtype=torch.float32)\n",
|
||
|
|
"test_targets = torch.tensor(test_sequences[ :, -1], dtype=torch.float32)\n",
|
||
|
|
"print(f\"test_inputs_shape{test_inputs.shape}\")\n",
|
||
|
|
"test_outputs = model(test_inputs)\n",
|
||
|
|
"test_loss = criterion(test_outputs, test_targets)\n",
|
||
|
|
"# print(test_outputs)\n",
|
||
|
|
"# print(test_targets)\n",
|
||
|
|
"print(f'Test Loss: {test_loss.item():.4f}')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 53,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Parameter containing:\n",
|
||
|
|
"tensor([[0.7608]], requires_grad=True)\n",
|
||
|
|
"Parameter containing:\n",
|
||
|
|
"tensor([-1.3928], requires_grad=True)\n",
|
||
|
|
"tensor([6.2156], grad_fn=<AddBackward0>)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(model.fc0.weight)\n",
|
||
|
|
"\n",
|
||
|
|
"one_point=torch.tensor([10],dtype=torch.float32)\n",
|
||
|
|
"print(model(one_point))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"读取点修正模型"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 67,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0.7511485815048218\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model.fc0.weight.tolist()[0][0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 70,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
" <script type=\"text/javascript\">\n",
|
||
|
|
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
|
||
|
|
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
||
|
|
" if (typeof require !== 'undefined') {\n",
|
||
|
|
" require.undef(\"plotly\");\n",
|
||
|
|
" requirejs.config({\n",
|
||
|
|
" paths: {\n",
|
||
|
|
" 'plotly': ['https://cdn.plot.ly/plotly-2.18.2.min']\n",
|
||
|
|
" }\n",
|
||
|
|
" });\n",
|
||
|
|
" require(['plotly'], function(Plotly) {\n",
|
||
|
|
" window._Plotly = Plotly;\n",
|
||
|
|
" });\n",
|
||
|
|
" }\n",
|
||
|
|
" </script>\n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.plotly.v1+json": {
|
||
|
|
"config": {
|
||
|
|
"linkText": "Export to plot.ly",
|
||
|
|
"plotlyServerURL": "https://plot.ly",
|
||
|
|
"showLink": false
|
||
|
|
},
|
||
|
|
"data": [
|
||
|
|
{
|
||
|
|
"mode": "lines+markers",
|
||
|
|
"type": "scatter",
|
||
|
|
"x": [
|
||
|
|
1,
|
||
|
|
2,
|
||
|
|
3,
|
||
|
|
4,
|
||
|
|
5
|
||
|
|
],
|
||
|
|
"y": [
|
||
|
|
1,
|
||
|
|
3,
|
||
|
|
2,
|
||
|
|
4,
|
||
|
|
3
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"layout": {
|
||
|
|
"template": {
|
||
|
|
"data": {
|
||
|
|
"bar": [
|
||
|
|
{
|
||
|
|
"error_x": {
|
||
|
|
"color": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"error_y": {
|
||
|
|
"color": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"marker": {
|
||
|
|
"line": {
|
||
|
|
"color": "#E5ECF6",
|
||
|
|
"width": 0.5
|
||
|
|
},
|
||
|
|
"pattern": {
|
||
|
|
"fillmode": "overlay",
|
||
|
|
"size": 10,
|
||
|
|
"solidity": 0.2
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"type": "bar"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"barpolar": [
|
||
|
|
{
|
||
|
|
"marker": {
|
||
|
|
"line": {
|
||
|
|
"color": "#E5ECF6",
|
||
|
|
"width": 0.5
|
||
|
|
},
|
||
|
|
"pattern": {
|
||
|
|
"fillmode": "overlay",
|
||
|
|
"size": 10,
|
||
|
|
"solidity": 0.2
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"type": "barpolar"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"carpet": [
|
||
|
|
{
|
||
|
|
"aaxis": {
|
||
|
|
"endlinecolor": "#2a3f5f",
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"minorgridcolor": "white",
|
||
|
|
"startlinecolor": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"baxis": {
|
||
|
|
"endlinecolor": "#2a3f5f",
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"minorgridcolor": "white",
|
||
|
|
"startlinecolor": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"type": "carpet"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"choropleth": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"type": "choropleth"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"contour": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"colorscale": [
|
||
|
|
[
|
||
|
|
0,
|
||
|
|
"#0d0887"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.1111111111111111,
|
||
|
|
"#46039f"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.2222222222222222,
|
||
|
|
"#7201a8"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.3333333333333333,
|
||
|
|
"#9c179e"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.4444444444444444,
|
||
|
|
"#bd3786"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.5555555555555556,
|
||
|
|
"#d8576b"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.6666666666666666,
|
||
|
|
"#ed7953"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.7777777777777778,
|
||
|
|
"#fb9f3a"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.8888888888888888,
|
||
|
|
"#fdca26"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
1,
|
||
|
|
"#f0f921"
|
||
|
|
]
|
||
|
|
],
|
||
|
|
"type": "contour"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"contourcarpet": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"type": "contourcarpet"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
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|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"colorscale": [
|
||
|
|
[
|
||
|
|
0,
|
||
|
|
"#0d0887"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.1111111111111111,
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||
|
|
"#46039f"
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||
|
|
],
|
||
|
|
[
|
||
|
|
0.2222222222222222,
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||
|
|
"#7201a8"
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||
|
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],
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||
|
|
[
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||
|
|
0.3333333333333333,
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||
|
|
"#9c179e"
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||
|
|
],
|
||
|
|
[
|
||
|
|
0.4444444444444444,
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||
|
|
"#bd3786"
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||
|
|
],
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|
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[
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|
|
0.5555555555555556,
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||
|
|
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||
|
|
],
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||
|
|
[
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||
|
|
0.6666666666666666,
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||
|
|
"#ed7953"
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||
|
|
],
|
||
|
|
[
|
||
|
|
0.7777777777777778,
|
||
|
|
"#fb9f3a"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.8888888888888888,
|
||
|
|
"#fdca26"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
1,
|
||
|
|
"#f0f921"
|
||
|
|
]
|
||
|
|
],
|
||
|
|
"type": "heatmap"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"heatmapgl": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"colorscale": [
|
||
|
|
[
|
||
|
|
0,
|
||
|
|
"#0d0887"
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||
|
|
],
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||
|
|
[
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||
|
|
0.1111111111111111,
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||
|
|
"#46039f"
|
||
|
|
],
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||
|
|
[
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||
|
|
0.2222222222222222,
|
||
|
|
"#7201a8"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.3333333333333333,
|
||
|
|
"#9c179e"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.4444444444444444,
|
||
|
|
"#bd3786"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.5555555555555556,
|
||
|
|
"#d8576b"
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||
|
|
],
|
||
|
|
[
|
||
|
|
0.6666666666666666,
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||
|
|
"#ed7953"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.7777777777777778,
|
||
|
|
"#fb9f3a"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.8888888888888888,
|
||
|
|
"#fdca26"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
1,
|
||
|
|
"#f0f921"
|
||
|
|
]
|
||
|
|
],
|
||
|
|
"type": "heatmapgl"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"histogram": [
|
||
|
|
{
|
||
|
|
"marker": {
|
||
|
|
"pattern": {
|
||
|
|
"fillmode": "overlay",
|
||
|
|
"size": 10,
|
||
|
|
"solidity": 0.2
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"type": "histogram"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"histogram2d": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"colorscale": [
|
||
|
|
[
|
||
|
|
0,
|
||
|
|
"#0d0887"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.1111111111111111,
|
||
|
|
"#46039f"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.2222222222222222,
|
||
|
|
"#7201a8"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.3333333333333333,
|
||
|
|
"#9c179e"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.4444444444444444,
|
||
|
|
"#bd3786"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.5555555555555556,
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||
|
|
"#d8576b"
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||
|
|
],
|
||
|
|
[
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||
|
|
0.6666666666666666,
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||
|
|
"#ed7953"
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||
|
|
],
|
||
|
|
[
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||
|
|
0.7777777777777778,
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||
|
|
"#fb9f3a"
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||
|
|
],
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||
|
|
[
|
||
|
|
0.8888888888888888,
|
||
|
|
"#fdca26"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
1,
|
||
|
|
"#f0f921"
|
||
|
|
]
|
||
|
|
],
|
||
|
|
"type": "histogram2d"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"histogram2dcontour": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"colorscale": [
|
||
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"ticks": "",
|
||
|
|
"zerolinecolor": "white"
|
||
|
|
},
|
||
|
|
"zaxis": {
|
||
|
|
"backgroundcolor": "#E5ECF6",
|
||
|
|
"gridcolor": "white",
|
||
|
|
"gridwidth": 2,
|
||
|
|
"linecolor": "white",
|
||
|
|
"showbackground": true,
|
||
|
|
"ticks": "",
|
||
|
|
"zerolinecolor": "white"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"shapedefaults": {
|
||
|
|
"line": {
|
||
|
|
"color": "#2a3f5f"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"ternary": {
|
||
|
|
"aaxis": {
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"baxis": {
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"bgcolor": "#E5ECF6",
|
||
|
|
"caxis": {
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"ticks": ""
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"title": {
|
||
|
|
"x": 0.05
|
||
|
|
},
|
||
|
|
"xaxis": {
|
||
|
|
"automargin": true,
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"ticks": "",
|
||
|
|
"title": {
|
||
|
|
"standoff": 15
|
||
|
|
},
|
||
|
|
"zerolinecolor": "white",
|
||
|
|
"zerolinewidth": 2
|
||
|
|
},
|
||
|
|
"yaxis": {
|
||
|
|
"automargin": true,
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"ticks": "",
|
||
|
|
"title": {
|
||
|
|
"standoff": 15
|
||
|
|
},
|
||
|
|
"zerolinecolor": "white",
|
||
|
|
"zerolinewidth": 2
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"text/html": [
|
||
|
|
"<div> <div id=\"aa0c0fd1-4f98-4b9a-9119-51b6010d1d5b\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div> <script type=\"text/javascript\"> require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById(\"aa0c0fd1-4f98-4b9a-9119-51b6010d1d5b\")) { Plotly.newPlot( \"aa0c0fd1-4f98-4b9a-9119-51b6010d1d5b\", [{\"mode\":\"lines+markers\",\"x\":[1,2,3,4,5],\"y\":[1,3,2,4,3],\"type\":\"scatter\"}], {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"mark
|
||
|
|
" \n",
|
||
|
|
"var gd = document.getElementById('aa0c0fd1-4f98-4b9a-9119-51b6010d1d5b');\n",
|
||
|
|
"var x = new MutationObserver(function (mutations, observer) {{\n",
|
||
|
|
" var display = window.getComputedStyle(gd).display;\n",
|
||
|
|
" if (!display || display === 'none') {{\n",
|
||
|
|
" console.log([gd, 'removed!']);\n",
|
||
|
|
" Plotly.purge(gd);\n",
|
||
|
|
" observer.disconnect();\n",
|
||
|
|
" }}\n",
|
||
|
|
"}});\n",
|
||
|
|
"\n",
|
||
|
|
"// Listen for the removal of the full notebook cells\n",
|
||
|
|
"var notebookContainer = gd.closest('#notebook-container');\n",
|
||
|
|
"if (notebookContainer) {{\n",
|
||
|
|
" x.observe(notebookContainer, {childList: true});\n",
|
||
|
|
"}}\n",
|
||
|
|
"\n",
|
||
|
|
"// Listen for the clearing of the current output cell\n",
|
||
|
|
"var outputEl = gd.closest('.output');\n",
|
||
|
|
"if (outputEl) {{\n",
|
||
|
|
" x.observe(outputEl, {childList: true});\n",
|
||
|
|
"}}\n",
|
||
|
|
"\n",
|
||
|
|
" }) }; }); </script> </div>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import plotly.graph_objs as go\n",
|
||
|
|
"from plotly.offline import iplot, init_notebook_mode\n",
|
||
|
|
"\n",
|
||
|
|
"# 初始化Plotly的Notebook模式\n",
|
||
|
|
"init_notebook_mode(connected=True)\n",
|
||
|
|
"\n",
|
||
|
|
"# 创建初始的折线图数据\n",
|
||
|
|
"x = [1, 2, 3, 4, 5]\n",
|
||
|
|
"y = [1, 3, 2, 4, 3]\n",
|
||
|
|
"\n",
|
||
|
|
"trace = go.Scatter(x=x, y=y, mode='lines+markers')\n",
|
||
|
|
"\n",
|
||
|
|
"data = [trace]\n",
|
||
|
|
"\n",
|
||
|
|
"# 绘制初始的折线图\n",
|
||
|
|
"fig = go.Figure(data=data)\n",
|
||
|
|
"\n",
|
||
|
|
"iplot(fig)\n",
|
||
|
|
"\n",
|
||
|
|
"# 动态添加新的点\n",
|
||
|
|
"new_x = [6]\n",
|
||
|
|
"new_y = [2]\n",
|
||
|
|
"fig.add_trace(go.Scatter(x=new_x, y=new_y, mode='markers'))\n",
|
||
|
|
"\n",
|
||
|
|
"# 更新折线图数据\n",
|
||
|
|
"fig.update_traces(x=[list(fig.data[0].x) + new_x],\n",
|
||
|
|
" y=[list(fig.data[0].y) + new_y])\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# iplot(fig)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 71,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.plotly.v1+json": {
|
||
|
|
"config": {
|
||
|
|
"plotlyServerURL": "https://plot.ly"
|
||
|
|
},
|
||
|
|
"data": [
|
||
|
|
{
|
||
|
|
"mode": "lines+markers",
|
||
|
|
"type": "scatter",
|
||
|
|
"x": [
|
||
|
|
[
|
||
|
|
1,
|
||
|
|
2,
|
||
|
|
3,
|
||
|
|
4,
|
||
|
|
5,
|
||
|
|
6
|
||
|
|
]
|
||
|
|
],
|
||
|
|
"y": [
|
||
|
|
[
|
||
|
|
1,
|
||
|
|
3,
|
||
|
|
2,
|
||
|
|
4,
|
||
|
|
3,
|
||
|
|
2
|
||
|
|
]
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"mode": "markers",
|
||
|
|
"type": "scatter",
|
||
|
|
"x": [
|
||
|
|
[
|
||
|
|
1,
|
||
|
|
2,
|
||
|
|
3,
|
||
|
|
4,
|
||
|
|
5,
|
||
|
|
6
|
||
|
|
]
|
||
|
|
],
|
||
|
|
"y": [
|
||
|
|
[
|
||
|
|
1,
|
||
|
|
3,
|
||
|
|
2,
|
||
|
|
4,
|
||
|
|
3,
|
||
|
|
2
|
||
|
|
]
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"layout": {
|
||
|
|
"template": {
|
||
|
|
"data": {
|
||
|
|
"bar": [
|
||
|
|
{
|
||
|
|
"error_x": {
|
||
|
|
"color": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"error_y": {
|
||
|
|
"color": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"marker": {
|
||
|
|
"line": {
|
||
|
|
"color": "#E5ECF6",
|
||
|
|
"width": 0.5
|
||
|
|
},
|
||
|
|
"pattern": {
|
||
|
|
"fillmode": "overlay",
|
||
|
|
"size": 10,
|
||
|
|
"solidity": 0.2
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"type": "bar"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"barpolar": [
|
||
|
|
{
|
||
|
|
"marker": {
|
||
|
|
"line": {
|
||
|
|
"color": "#E5ECF6",
|
||
|
|
"width": 0.5
|
||
|
|
},
|
||
|
|
"pattern": {
|
||
|
|
"fillmode": "overlay",
|
||
|
|
"size": 10,
|
||
|
|
"solidity": 0.2
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"type": "barpolar"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"carpet": [
|
||
|
|
{
|
||
|
|
"aaxis": {
|
||
|
|
"endlinecolor": "#2a3f5f",
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"minorgridcolor": "white",
|
||
|
|
"startlinecolor": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"baxis": {
|
||
|
|
"endlinecolor": "#2a3f5f",
|
||
|
|
"gridcolor": "white",
|
||
|
|
"linecolor": "white",
|
||
|
|
"minorgridcolor": "white",
|
||
|
|
"startlinecolor": "#2a3f5f"
|
||
|
|
},
|
||
|
|
"type": "carpet"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"choropleth": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"type": "choropleth"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"contour": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"colorscale": [
|
||
|
|
[
|
||
|
|
0,
|
||
|
|
"#0d0887"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.1111111111111111,
|
||
|
|
"#46039f"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.2222222222222222,
|
||
|
|
"#7201a8"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.3333333333333333,
|
||
|
|
"#9c179e"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.4444444444444444,
|
||
|
|
"#bd3786"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.5555555555555556,
|
||
|
|
"#d8576b"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.6666666666666666,
|
||
|
|
"#ed7953"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.7777777777777778,
|
||
|
|
"#fb9f3a"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.8888888888888888,
|
||
|
|
"#fdca26"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
1,
|
||
|
|
"#f0f921"
|
||
|
|
]
|
||
|
|
],
|
||
|
|
"type": "contour"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"contourcarpet": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"type": "contourcarpet"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"heatmap": [
|
||
|
|
{
|
||
|
|
"colorbar": {
|
||
|
|
"outlinewidth": 0,
|
||
|
|
"ticks": ""
|
||
|
|
},
|
||
|
|
"colorscale": [
|
||
|
|
[
|
||
|
|
0,
|
||
|
|
"#0d0887"
|
||
|
|
],
|
||
|
|
[
|
||
|
|
0.1111111111111111,
|
||
|
|
"#46039f"
|
||
|
|
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"title": {
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"<div> <div id=\"f1e976b7-a562-4af8-8830-7e27ba5546de\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div> <script type=\"text/javascript\"> require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById(\"f1e976b7-a562-4af8-8830-7e27ba5546de\")) { Plotly.newPlot( \"f1e976b7-a562-4af8-8830-7e27ba5546de\", [{\"mode\":\"lines+markers\",\"x\":[[1,2,3,4,5,6]],\"y\":[[1,3,2,4,3,2]],\"type\":\"scatter\"},{\"mode\":\"markers\",\"x\":[[1,2,3,4,5,6]],\"y\":[[1,3,2,4,3,2]],\"type\":\"scatter\"}], {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scat
|
||
|
|
" \n",
|
||
|
|
"var gd = document.getElementById('f1e976b7-a562-4af8-8830-7e27ba5546de');\n",
|
||
|
|
"var x = new MutationObserver(function (mutations, observer) {{\n",
|
||
|
|
" var display = window.getComputedStyle(gd).display;\n",
|
||
|
|
" if (!display || display === 'none') {{\n",
|
||
|
|
" console.log([gd, 'removed!']);\n",
|
||
|
|
" Plotly.purge(gd);\n",
|
||
|
|
" observer.disconnect();\n",
|
||
|
|
" }}\n",
|
||
|
|
"}});\n",
|
||
|
|
"\n",
|
||
|
|
"// Listen for the removal of the full notebook cells\n",
|
||
|
|
"var notebookContainer = gd.closest('#notebook-container');\n",
|
||
|
|
"if (notebookContainer) {{\n",
|
||
|
|
" x.observe(notebookContainer, {childList: true});\n",
|
||
|
|
"}}\n",
|
||
|
|
"\n",
|
||
|
|
"// Listen for the clearing of the current output cell\n",
|
||
|
|
"var outputEl = gd.closest('.output');\n",
|
||
|
|
"if (outputEl) {{\n",
|
||
|
|
" x.observe(outputEl, {childList: true});\n",
|
||
|
|
"}}\n",
|
||
|
|
"\n",
|
||
|
|
" }) }; }); </script> </div>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# for epoch in range(num_epochs):\n",
|
||
|
|
"# for i in range(0, len(train_sequences), batch_size):\n",
|
||
|
|
"# batch_sequences = train_sequences[i:i+batch_size]\n",
|
||
|
|
"# # print(batch_sequences.shape)\n",
|
||
|
|
"# inputs = torch.tensor(batch_sequences[:, :-1], dtype=torch.float32)\n",
|
||
|
|
" \n",
|
||
|
|
"# targets = torch.tensor(batch_sequences[:,-1], dtype=torch.float32)\n",
|
||
|
|
"# if len(inputs) != batch_size:\n",
|
||
|
|
"# continue\n",
|
||
|
|
" \n",
|
||
|
|
"# # 前向传播\n",
|
||
|
|
"# outputs = model(inputs)\n",
|
||
|
|
"# # print(f\"targets {targets}\")\n",
|
||
|
|
" \n",
|
||
|
|
"# # print(f\"input:{inputs}\")\n",
|
||
|
|
" \n",
|
||
|
|
"# # print(f\"outputs {outputs}\")\n",
|
||
|
|
" \n",
|
||
|
|
"# outputs=outputs.reshape(targets.shape)\n",
|
||
|
|
"# # print(outputs.shape)\n",
|
||
|
|
"# # print(f\"output:{outputs.shape}\")\n",
|
||
|
|
"# # print(f\"target:{targets.shape}\")\n",
|
||
|
|
"# loss = criterion(outputs, targets)\n",
|
||
|
|
"# # print(f\"loss:{loss}\")\n",
|
||
|
|
"# # loss=loss*1000\n",
|
||
|
|
"# # 反向传播和优化\n",
|
||
|
|
"# loss.backward()\n",
|
||
|
|
"# optimizer.step()\n",
|
||
|
|
"# optimizer.zero_grad()\n",
|
||
|
|
" \n",
|
||
|
|
" \n",
|
||
|
|
" \n",
|
||
|
|
"# # if epoch>996:\n",
|
||
|
|
"# if True:\n",
|
||
|
|
"# print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')\n",
|
||
|
|
" \n",
|
||
|
|
"# print(model.fc0.weight.tolist())\n",
|
||
|
|
"# print(model.fc0.bias)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 72,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"torch.save(model.state_dict(), 'lstm_model.pt')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"读取"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "",
|
||
|
|
"evalue": "",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||
|
|
"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model = LSTM(input_size, hidden_size, num_layers, output_size)\n",
|
||
|
|
"\n",
|
||
|
|
"model.load_state_dict(torch.load('lstm_model.pt'))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"下面是尝试反缩放归一化的数据并在测试集测试，发现误差很大，所以决定不再缩放"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "",
|
||
|
|
"evalue": "",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[1;31mnotebook controller is DISPOSED. \n",
|
||
|
|
"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"model.eval()\n",
|
||
|
|
"test_inputs = torch.tensor(test_sequences[:, :-1,:-1], dtype=torch.float32)\n",
|
||
|
|
"test_targets = torch.tensor(test_sequences[:, -1, -1], dtype=torch.float32)\n",
|
||
|
|
"test_outputs = model(test_inputs)\n",
|
||
|
|
"\n",
|
||
|
|
"print(test_outputs.shape)\n",
|
||
|
|
"print(test_outputs.size())\n",
|
||
|
|
"t=np.zeros((test_outputs.shape[0],1),dtype=float)\n",
|
||
|
|
"\n",
|
||
|
|
"t=torch.Tensor(t)\n",
|
||
|
|
"print(t.shape)\n",
|
||
|
|
"test_outputs=torch.cat([t, test_outputs], dim=1)\n",
|
||
|
|
"print(test_outputs.shape)\n",
|
||
|
|
"\n",
|
||
|
|
"test_outputs = scaler.inverse_transform(test_outputs.detach().numpy() )\n",
|
||
|
|
"test_targets = scaler.inverse_transform(torch.tensor(test_sequences[:, -1, :]).detach().numpy())\n",
|
||
|
|
"test_outputs=torch.tensor(test_outputs[:,-1], dtype=torch.float32)\n",
|
||
|
|
"test_targets=torch.tensor(test_targets[:,-1], dtype=torch.float32)\n",
|
||
|
|
"print(test_outputs)\n",
|
||
|
|
"print(test_targets)\n",
|
||
|
|
"# print(test_outputs.shape)\n",
|
||
|
|
"# print(test_targets.shape)\n",
|
||
|
|
"\n",
|
||
|
|
"test_loss = criterion(test_outputs, test_targets)\n",
|
||
|
|
"print(f'Test Loss: {test_loss:.4f}')\n",
|
||
|
|
"# a=test_outputs>0.00007\n",
|
||
|
|
"# print(torch.sum(a))\n",
|
||
|
|
"# b=test_targets>0.00007\n",
|
||
|
|
"# print(torch.sum(b))\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3.9.16 ('torch_py3.9')",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.16"
|
||
|
|
},
|
||
|
|
"orig_nbformat": 4,
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "2c93e8e09754cf1cd40b55c7c8fefeaff6ac58d6a85dd394fdad2eae466f5690"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|