mhjensen
39 lines
1.6 KiB
Python
39 lines
1.6 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from IPython.display import display
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import sklearn
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from sklearn.linear_model import LinearRegression
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from sklearn.tree import DecisionTreeRegressor
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from sklearn.model_selection import train_test_split
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import mglearn
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X, y = mglearn.datasets.make_forge()
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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from sklearn.neighbors import KNeighborsClassifier
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clf = KNeighborsClassifier(n_neighbors=3)
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clf.fit(X_train, y_train)
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KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',metric_params=None, n_jobs=1, n_neighbors=3, p=2,weights='uniform')
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clf.predict(X_test)
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clf.score(X_test, y_test)
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fig, axes = plt.subplots(1, 3, figsize=(10, 3))
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for n_neighbors, ax in zip([1, 3, 9], axes):
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clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X, y)
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mglearn.plots.plot_2d_separator(clf, X, fill=True, eps=0.5, ax=ax, alpha=.4)
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ax.scatter(X[:, 0], X[:, 1], c=y, s=60, cmap=mglearn.cm2)
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ax.set_title("%d neighbor(s)" % n_neighbors)
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data = np.loadtxt('src/Hudson_Bay.csv', delimiter=',', skiprows=1)
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x = data[:,0]
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y = data[:,1]
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#x_train, y_train = train_test_split(x, y, random_state=0)
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line = np.linspace(1900,1930,1000,endpoint=False).reshape(-1,1)
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reg = DecisionTreeRegressor(min_samples_split=3).fit(x.reshape(-1,1),y.reshape(-1,1))
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plt.plot(line, reg.predict(line), label="decision tree")
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regline = LinearRegression().fit(x.reshape(-1,1),y.reshape(-1,1))
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plt.plot(line, regline.predict(line), label= "Linear Regression")
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plt.plot(x, y, label= "Linear Regression")
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plt.show()
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