六：SVM

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
from scipy.io import loadmat

#第一个文件
raw_data = loadmat(r'******')
#用散点图表示，其中类标签由符号表示（+表示正类，o表示负类）
data = pd.DataFrame(raw_data['X'], columns=['X1', 'X2'])
data['y'] = raw_data['y']

positive = data[data['y'].isin([1])]
negative = data[data['y'].isin([0])]

fig, ax = plt.subplots(figsize=(12,8))
ax.scatter(positive['X1'], positive['X2'], s=50, marker='x', label='Positive')
ax.scatter(negative['X1'], negative['X2'], s=50, marker='o', label='Negative')
ax.legend()
# plt.show()

#使用skleaern
from sklearn import svm

# #c=1的结果
# svc = svm.LinearSVC(C=1, loss='hinge', max_iter=100000)
# svc.fit(data[['X1', 'X2']], data['y']) #根据给定的训练数据集合SVM模型
# print(svc.score(data[['X1', 'X2']], data['y']))
#
# #c= 100的结果
# svc2 = svm.LinearSVC(C=100, loss='hinge', max_iter=100000)
# svc2.fit(data[['X1', 'X2']], data['y'])
# print(svc2.score(data[['X1', 'X2']], data['y']))
#
# data['SVM 1 Confidence'] = svc.decision_function(data[['X1', 'X2']])
#
# fig, ax = plt.subplots(figsize=(12,8))
# ax.scatter(data['X1'], data['X2'], s=50, c=data['SVM 1 Confidence'], cmap='seismic')
# ax.set_title('SVM (C=1) Decision Confidence')
# plt.show()
#
# data['SVM 2 Confidence'] = svc2.decision_function(data[['X1', 'X2']])
#
# fig, ax = plt.subplots(figsize=(12,8))
# ax.scatter(data['X1'], data['X2'], s=50, c=data['SVM 2 Confidence'], cmap='seismic')
# ax.set_title('SVM (C=100) Decision Confidence')
# plt.show()

def gaussian_kernel(x1, x2, sigma):
    return np.exp(-(np.sum((x1 - x2) ** 2) / (2 * (sigma ** 2))))

x1 = np.array([1.0, 2.0, 1.0])
x2 = np.array([0.0, 4.0, -1.0])
sigma = 2
print(gaussian_kernel(x1, x2, sigma))

#第二个文件
raw_data = loadmat(r'******')

data = pd.DataFrame(raw_data['X'], columns=['X1', 'X2'])
data['y'] = raw_data['y']

positive = data[data['y'].isin([1])]
negative = data[data['y'].isin([0])]

fig, ax = plt.subplots(figsize=(12,8))
ax.scatter(positive['X1'], positive['X2'], s=30, marker='x', label='Positive')
ax.scatter(negative['X1'], negative['X2'], s=30, marker='o', label='Negative')
ax.legend()
plt.show()
svc = svm.SVC(C=100, gamma=10, probability=True)
svc.fit(data[['X1', 'X2']], data['y'])
svc.score(data[['X1', 'X2']], data['y'])
data['Probability'] = svc.predict_proba(data[['X1', 'X2']])[:,0]

fig, ax = plt.subplots(figsize=(12,8))
ax.scatter(data['X1'], data['X2'], s=30, c=data['Probability'], cmap='Reds')
plt.show()