supervised class regression

Author: lazyprogrammer

supervised_class/regression.py

@@ -0,0 +1,30 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.tree import DecisionTreeRegressor
+
+N = 200
+X = np.linspace(0, 10, N).reshape(N, 1)
+Y = np.sin(X)
+
+Ntrain = 20
+idx = np.random.choice(N, Ntrain)
+Xtrain = X[idx]
+Ytrain = Y[idx]
+
+knn = KNeighborsRegressor(n_neighbors=2, weights='distance')
+knn.fit(Xtrain, Ytrain)
+Yknn = knn.predict(X)
+
+dt = DecisionTreeRegressor()
+dt.fit(Xtrain, Ytrain)
+Ydt = dt.predict(X)
+
+plt.scatter(Xtrain, Ytrain) # show the training points
+plt.plot(X, Y) # show the original data
+plt.plot(X, Yknn, label='KNN')
+plt.plot(X, Ydt, label='Decision Tree')
+plt.legend()
+plt.show()
+
+