added KNN using sklearn

KNN/iris_sklearn.py

@@ -0,0 +1,84 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn import datasets 
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+neigh = KNeighborsClassifier(n_neighbors=3)
+
+
+iris = datasets.load_iris()
+X = iris.data[:, :2]  # we only take the first two features.
+y = iris.target
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=32)
+
+
+
+
+
+predicts=[]
+for i in range(1,20):
+
+    neigh = KNeighborsClassifier(n_neighbors=i)
+    neigh.fit(X_train,y_train)
+    predicts.append(accuracy_score(y_test,neigh.predict(X_test)))
+
+xpre=np.arange(1,20)
+
+neigh = KNeighborsClassifier(n_neighbors=8)
+neigh.fit(X, y)
+
+####
+x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
+y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
+
+
+h = .02  # step size in the mesh
+x_min, x_max = X[:, 0].min() - 0.2, X[:, 0].max() + 0.2
+y_min, y_max = X[:, 1].min() - 0.2, X[:, 1].max() + 0.2
+xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
+
+
+xnew=np.concatenate((xx.ravel().reshape((28200,1)),yy.ravel().reshape((28200,1))),axis=1)
+print(xnew.shape)
+
+ynew=np.asarray(neigh.predict(xnew))
+print(ynew.shape)
+
+plt.figure(2, figsize=(12, 6))
+
+plt.clf()
+
+plt.subplot(121,aspect='equal')
+
+plt.scatter(xnew[:,0],xnew[:,1],c=ynew,cmap='brg')
+
+
+
+
+
+
+
+
+plt.scatter(X[:, 0], X[:, 1], c=y,s=40,cmap='bone')
+
+plt.xlabel('Sepal length')
+plt.ylabel('Sepal width')
+
+plt.xlim(x_min, x_max)
+plt.ylim(y_min, y_max)
+plt.xticks(())
+plt.yticks(())
+
+plt.subplot(122)
+plt.xlabel('K')
+plt.ylabel('Accuracy')
+plt.plot(xpre,np.asarray(predicts))
+plt.xticks(xpre)
+plt.yticks(predicts)
+plt.show()
+
+
+
+