%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
iris = load_iris()
# print(iris.DESCR)
X = iris.data
Y = iris.target
X[0]
array([ 5.1, 3.5, 1.4, 0.2])
X[0][2:]
array([ 1.4, 0.2])
X = X[:, 2:]
X[0]
array([ 1.4, 0.2])
Y
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, Y,
test_size=0.2,
random_state=87)
plt.scatter(x_train[:,0], x_train[:,1], c=y_train)
<matplotlib.collections.PathCollection at 0x11a7794e0>
from sklearn.svm import SVC
clf = SVC()
clf.fit(x_train, y_train)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=None, degree=3, gamma='auto', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
y_predict = clf.predict(x_test)
plt.scatter(x_test[:,0], x_test[:,1], c=y_predict)
<matplotlib.collections.PathCollection at 0x11ad009b0>
plt.scatter(x_test[:,0], x_test[:,1], c=y_predict - y_test)
<matplotlib.collections.PathCollection at 0x11ad6b630>
np.arange(0.3,10.3,0.2)
array([ 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5, 1.7, 1.9, 2.1, 2.3, 2.5, 2.7, 2.9, 3.1, 3.3, 3.5, 3.7, 3.9, 4.1, 4.3, 4.5, 4.7, 4.9, 5.1, 5.3, 5.5, 5.7, 5.9, 6.1, 6.3, 6.5, 6.7, 6.9, 7.1, 7.3, 7.5, 7.7, 7.9, 8.1, 8.3, 8.5, 8.7, 8.9, 9.1, 9.3, 9.5, 9.7, 9.9, 10.1])
x1, x2 = np.meshgrid(np.arange(0,7,0.02), np.arange(0,3,0.02a
xx = [1,2,3,4]
yy = [5,6,7,8]
np.c_[xx, yy]
array([[1, 5], [2, 6], [3, 7], [4, 8]])
Z = clf.predict(np.c_[x1.ravel(), x2.ravel()])
Z = Z.reshape(x1.shape)
plt.contourf(x1, x2, Z, cmap=plt.cm.coolwarm, alpha=0.8)
plt.scatter(X[:,0], X[:,1], c=Y)
<matplotlib.collections.PathCollection at 0x11d9f0390>