51 lines
2.5 KiB
Python
51 lines
2.5 KiB
Python
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import random
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import numpy as np
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from matplotlib import pyplot as plt
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from matplotlib.colors import ListedColormap
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.datasets import make_moons, make_circles, make_classification
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from sklearn.neural_network import MLPClassifier
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from sklearn.linear_model import LinearRegression
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.linear_model import Perceptron
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rs = random.randrange(100)
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X, y = make_moons(n_samples=200, noise=0.3, random_state=rs)
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X_train, X_Test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
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print("Линейная регрессия")
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linerModel = LinearRegression().fit(X_train, y_train)
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print("результат модели на учебных данных =", linerModel.score(X_train, y_train))
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print("результат модели на тестовых данных =", linerModel.score(X_Test, y_test))
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print("Многослойный персептрон с 10-ю нейронами в скрытом слое")
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mlp = MLPClassifier(hidden_layer_sizes=(10), alpha = 0.01, max_iter=2000).fit(X_train, y_train)
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print("результат модели на учебных данных =", mlp.score(X_train, y_train))
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print("результат модели на тестовых данных =", mlp.score(X_Test, y_test))
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print("Персептрон ")
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perceptron = Perceptron().fit(X_train, y_train)
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print("результат модели на учебных данных =", perceptron.score(X_train, y_train))
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print("результат модели на тестовых данных =", perceptron.score(X_Test, y_test))
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plt.xlabel("Свойство 1")
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plt.ylabel("Свойство 2")
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plt.title("Сгенерированные данные")
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plt.scatter(X[:, 0], X[:, 1], c = y, cmap = plt.cm.Set1)
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plt.show()
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h = 0.01
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x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
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y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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def showPlot(name, model):
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plt.title(name)
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c = model.predict(np.c_[xx.ravel(), yy.ravel()])
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c = np.where(c >= 0.5, 1, 0)
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c = c.reshape(xx.shape)
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plt.contourf(xx, yy, c, cmap = plt.cm.Set1, alpha=0.2)
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plt.scatter(X[:, 0], X[:, 1], c = y, cmap = plt.cm.Set1)
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plt.show()
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showPlot("Линейная регрессия", linerModel)
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showPlot("Многослойный персептрон", mlp)
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showPlot("Персептрон", perceptron)
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