Update on r codes

doc/src/How2ReadData/test.py

@@ -5,12 +5,29 @@ import sklearn
 from sklearn.linear_model import LinearRegression
 from sklearn.tree import DecisionTreeRegressor
 from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)
+import mglearn
+X, y = mglearn.datasets.make_forge()
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+from sklearn.neighbors import KNeighborsClassifier
+clf = KNeighborsClassifier(n_neighbors=3)
+clf.fit(X_train, y_train)
+KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',metric_params=None, n_jobs=1, n_neighbors=3, p=2,weights='uniform')
+
+clf.predict(X_test)
+
+clf.score(X_test, y_test)
+fig, axes = plt.subplots(1, 3, figsize=(10, 3))
+for n_neighbors, ax in zip([1, 3, 9], axes):
+        clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X, y)
+        mglearn.plots.plot_2d_separator(clf, X, fill=True, eps=0.5, ax=ax, alpha=.4)
+        ax.scatter(X[:, 0], X[:, 1], c=y, s=60, cmap=mglearn.cm2)
+        ax.set_title("%d neighbor(s)" % n_neighbors)
+
 
 data = np.loadtxt('src/Hudson_Bay.csv', delimiter=',', skiprows=1)
 x = data[:,0]
 y = data[:,1]
-x_train,  y_train = train_test_split(x, y, random_state=0)
+#x_train,  y_train = train_test_split(x, y, random_state=0)
 line = np.linspace(1900,1930,1000,endpoint=False).reshape(-1,1)
 reg = DecisionTreeRegressor(min_samples_split=3).fit(x.reshape(-1,1),y.reshape(-1,1))
 plt.plot(line, reg.predict(line), label="decision tree")