def init_with_data(self, is_load=True, max_outgoing=10, max_airports=500, silent=False): ld = LoadData(self, is_load, max_outgoing, max_airports, silent) ld.load() silent or self.print_info()
from sklearn.preprocessing import StandardScaler import numpy as np from load_data import LoadData import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torch.utils.data import TensorDataset, DataLoader import csv def s(_data): print(type(_data),np.shape(_data)) load_data = LoadData() load_data.load() # データ読み込み # iris = datasets.load_iris() # data = iris.data # target = iris.target target = load_data.target data = load_data.data with open('_target.csv', 'w') as f: writer = csv.writer(f) for i in target: writer.writerow([i]) with open('_data.csv', 'w') as f: writer = csv.writer(f) writer.writerows(data) # 学習データと検証データに分割
# -*- coding: utf-8 -*- import torch import torch.nn as nn import torch.optim as optim from load_data import LoadData import numpy as np from torch.utils.data import TensorDataset # デバッグ用 def info(string, _data): print(string + '=> 型:'+str(type(_data))+', 形状:'+str(np.shape(_data))) # データの読み込み data = LoadData() data.load() _DIR_HERE = data.dir_here # 学習データと検証データに分割 x_train, x_valid, y_train, y_valid = train_test_split(data.input_train, data.correct_train, shuffle=True) input_data = torch.from_numpy(data.input_train.astype(np.float32)) correct_data = torch.from_numpy(data.correct_train.astype(np.float32)) info('input_data', input_data) info('correct_data', correct_data) print(correct_data[:100]) #NNの定義 n_in = np.shape(data.input_train)[1] n_mid = n_in * 4 n_out = data.n_out model = nn.Sequential( nn.Linear(n_in,n_mid),
h = holidays.Germany() def plot_df(df, **kwargs): dfy = df[df["Datum"].dt.year.between(2018, 2019)] # dfy = dfy[dfy["Datum"].dt.weekday < 5] # dfy = dfy[dfy.apply(lambda x: x["Datum"] not in h, axis=1)] dfy = dfy[dfy.apply(lambda x: x["Datum"] in h or x["Datum"].weekday() > 4, axis=1)] dg = dfy.groupby(dfy["Datum"].dt.month).mean() plt.plot(dg, **kwargs) pendlerstrecken = [1, 2, 4, 5, 6, 13] freizeitstrecken = [7, 9, 10, 11, 12] files = LoadData.load(freizeitstrecken) avg_df = pd.DataFrame({"Datum": [], "Zaehlerstand": []}) for key, data in files.items(): avg_df = avg_df.append(data) plot_df(data, label=LoadData.NAMINGS[key], linestyle="dotted", linewidth=1.5) plot_df(avg_df, label="Durchschnitt", linestyle="solid", linewidth=3.0, c="k") plt.ylim(0) plt.xlim(1, 12) plt.grid(True) plt.legend()