# coding: utf-8
#1 sklearn简单例子from sklearn import svmX = [[2, 0], [1, 1], [2,3]]
y = [0, 0, 1]
clf = svm.SVC(kernel = 'linear')
clf.fit(X, y)print(clf)# get support vectors
print(clf.support_vectors_)# get indices of support vectors
print(clf.support_)# get number of support vectors for each class
print(clf.n_support_)# coding: utf-8
#2 sklearn画出决定界限
print(__doc__)import numpy as np
import pylab as pl
from sklearn import svm# we create 40 separable points
np.random.seed(0)
#随机数据
X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
#数据标签
label = [0] * 20 + [1] * 20
print(label)# fit the model
clf = svm.SVC(kernel='linear')
clf.fit(X, label)# get the separating hyperplane
w = clf.coef_[0]
a = -w[0] / w[1]
wb = clf.intercept_[0]
print( "w: ", w)
print( "a: ", a)
print("wb: ", wb)#超平面方程求解
# w[0] * x + w[1] * y + wb = 0
# y = (-w[0] / w[1]) * x - wb / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - (clf.intercept_[0]) / w[1]#支撑平面求解
# plot the parallels to the separating hyperplane that pass through the
# support vectors
# y = a * x + b
spoint = clf.support_vectors_[0]#获取分类为0的支持向量点
# x = spoint[0] y = spoint[1]; spoint[1] = a * spoint[0] = b
yy_down = a * xx + (spoint[1] - a * spoint[0])spoint = clf.support_vectors_[-1]#获取分类为1的支持向量点
yy_up = a * xx + (spoint[1] - a * spoint[0])# print( " xx: ", xx)
# print( " yy: ", yy)
print( "support_vectors_: ", clf.support_vectors_)
print( "clf.coef_: ", clf.coef_)# In scikit-learn coef_ attribute holds the vectors of the separating hyperplanes for linear models. It has shape (n_classes, n_features) if n_classes > 1 (multi-class one-vs-all) and (1, n_features) for binary classification.
#
# In this toy binary classification example, n_features == 2, hence w = coef_[0] is the vector orthogonal to the hyperplane (the hyperplane is fully defined by it + the intercept).
#
# To plot this hyperplane in the 2D case (any hyperplane of a 2D plane is a 1D line), we want to find a f as in y = f(x) = a.x + b. In this case a is the slope of the line and can be computed by a = -w[0] / w[1].#分割平面
# plot the line, the points, and the nearest vectors to the plane
pl.plot(xx, yy, 'k-')
pl.plot(xx, yy_down, 'k--')
pl.plot(xx, yy_up, 'k--')#支持向量点 黄色粗笔
pl.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80, c='y', cmap=pl.cm.Paired)
#数据点
pl.scatter(X[:, 0], X[:, 1], s = 10, c=label, cmap=pl.cm.Paired)pl.axis('tight')
pl.show()

"""
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB).. _LFW: http://vis-www.cs.umass.edu/lfw/Expected results for the top 5 most represented people in the dataset:================== ============ ======= ========== =======precision    recall  f1-score   support
================== ============ ======= ========== =======Ariel Sharon       0.67      0.92      0.77        13Colin Powell       0.75      0.78      0.76        60Donald Rumsfeld       0.78      0.67      0.72        27George W Bush       0.86      0.86      0.86       146
Gerhard Schroeder       0.76      0.76      0.76        25Hugo Chavez       0.67      0.67      0.67        15Tony Blair       0.81      0.69      0.75        36avg / total       0.80      0.80      0.80       322
================== ============ ======= ========== ======="""
from __future__ import print_functionfrom time import time
import logging
import matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import fetch_lfw_people
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import PCA
from sklearn.svm import SVCprint(__doc__)# Display progress logs on stdout
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')# #############################################################################
# Download the data, if not already on disk and load it as numpy arrays

lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = lfw_people.images.shape# for machine learning we use the 2 data directly (as relative pixel
# positions info is ignored by this model)
X = lfw_people.data
n_features = X.shape[1]# the label to predict is the id of the person
y = lfw_people.target
target_names = lfw_people.target_names
n_classes = target_names.shape[0]print("Total dataset size:")
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)# #############################################################################
# Split into a training set and a test set using a stratified k fold# split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# #############################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150print("Extracting the top %d eigenfaces from %d faces"% (n_components, X_train.shape[0]))
t0 = time()
pca = PCA(n_components=n_components, svd_solver='randomized',whiten=True).fit(X_train)
print("done in %0.3fs" % (time() - t0))eigenfaces = pca.components_.reshape((n_components, h, w))print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in %0.3fs" % (time() - t0))# #############################################################################
# Train a SVM classification modelprint("Fitting the classifier to the training set")
t0 = time()
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
print(clf.best_estimator_)# #############################################################################
# Quantitative evaluation of the model quality on the test setprint("Predicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
print("done in %0.3fs" % (time() - t0))print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))# #############################################################################
# Qualitative evaluation of the predictions using matplotlibdef plot_gallery(images, titles, h, w, n_row=3, n_col=4):"""Helper function to plot a gallery of portraits"""plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)for i in range(n_row * n_col):plt.subplot(n_row, n_col, i + 1)plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)plt.title(titles[i], size=12)plt.xticks(())plt.yticks(())# plot the result of the prediction on a portion of the test setdef title(y_pred, y_test, target_names, i):pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]return 'predicted: %s\ntrue:      %s' % (pred_name, true_name)prediction_titles = [title(y_pred, y_test, target_names, i)for i in range(y_pred.shape[0])]plot_gallery(X_test, prediction_titles, h, w)# plot the gallery of the most significative eigenfaces

eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)plt.show()