99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
import random
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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.datasets import make_moons
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from sklearn.linear_model import LinearRegression, Ridge
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.pipeline import Pipeline
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def start():
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rs = random.randrange(10)
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rs = 5
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X, y = make_moons(n_samples=250, 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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lin = LinearRegression()
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poly = Pipeline([('poly', PolynomialFeatures(degree=3)),
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('linear', LinearRegression())])
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ridge = Pipeline([('poly', PolynomialFeatures(degree=3)),
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('ridge', Ridge(alpha=1.0))])
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figure = plt.figure(1, figsize=(16, 9))
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axis = figure.subplots(4, 3)
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cm = ListedColormap(['#FF0000', "#0000FF"])
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arr_res = list(range(len(y_test)))
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X_scale = list(range(len(y_test)))
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lin.fit(X_train, y_train)
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res_y = lin.predict(X_test)
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print(lin.score(X_test, y_test))
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axis[0, 0].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm)
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axis[1, 0].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
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axis[3, 0].plot([i for i in range(len(res_y))], y_test, c="g")
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axis[3, 0].plot([i for i in range(len(res_y))], res_y, c="r")
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for i in range(len(X_test)):
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arr_res[i] = [X_test[i], res_y[i], y_test[i]]
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arr_res = sorted(arr_res, key=lambda x: x[1])
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for i in range(len(X_test)):
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X_scale[i] = arr_res[i][0]
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res_y[i] = arr_res[i][1]
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arr_res[i] = arr_res[i][2]
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axis[2, 0].plot(X_scale, arr_res, c="g")
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axis[2, 0].plot(X_scale, res_y, c="r")
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poly.fit(X_train, y_train)
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res_y = poly.predict(X_test)
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print(poly.score(X_test, y_test))
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axis[0, 1].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm)
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axis[1, 1].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
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axis[3, 1].plot([i for i in range(len(res_y))], y_test, c="g")
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axis[3, 1].plot([i for i in range(len(res_y))], res_y, c="r")
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for i in range(len(X_test)):
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arr_res[i] = [X_test[i], res_y[i], y_test[i]]
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arr_res = sorted(arr_res, key=lambda x: x[1])
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for i in range(len(X_test)):
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X_scale[i] = arr_res[i][0]
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res_y[i] = arr_res[i][1]
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arr_res[i] = arr_res[i][2]
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axis[2, 1].plot(X_scale, arr_res, c="g")
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axis[2, 1].plot(X_scale, res_y, c="r")
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ridge.fit(X_train, y_train)
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res_y = ridge.predict(X_test)
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print(ridge.score(X_test, y_test))
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axis[0, 2].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm)
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axis[1, 2].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
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axis[3, 2].plot([i for i in range(len(res_y))], y_test, c="g")
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axis[3, 2].plot([i for i in range(len(res_y))], res_y, c="r")
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for i in range(len(X_test)):
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arr_res[i] = [X_test[i], res_y[i], y_test[i]]
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arr_res = sorted(arr_res, key=lambda x: x[1])
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for i in range(len(X_test)):
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X_scale[i] = arr_res[i][0]
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res_y[i] = arr_res[i][1]
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arr_res[i] = arr_res[i][2]
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axis[2, 2].plot(X_scale, arr_res, c="g")
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axis[2, 2].plot(X_scale, res_y, c="r")
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plt.show()
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start()
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