import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

data

def create_data():

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['label'] = iris.target
df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']
data = np.array(df.iloc[:100, [0, 1, -1]])
for i in range(len(data)):
    if data[i, -1] == 0:
        data[i, -1] = -1
# print(data)
return data[:, :2], data[:, -1]

class SVM:

def __init__(self, max_iter=100, kernel='linear'):
    self.max_iter = max_iter
    self._kernel = kernel
def init_args(self, features, labels):
    self.m, self.n = features.shape
    self.X = features
    self.Y = labels
    self.b = 0.0
    # 将 Ei 保留在一个列表里
    self.alpha = np.ones(self.m)
    self.E = [self._E(i) for i in range(self.m)]
    # 松弛变量
    self.C = 1.0
def _KKT(self, i):
    y_g = self._g(i) * self.Y[i]
    if self.alpha[i] == 0:
        return y_g >= 1
    elif 0 < self.alpha[i] < self.C:
        return y_g == 1
    else:
        return y_g <= 1
# g(x) 预测值，输出 xi（X[i]）def _g(self, i):
    r = self.b
    for j in range(self.m):
        r += self.alpha[j] * self.Y[j] * self.kernel(self.X[i], self.X[j])
    return r
# 核函数
def kernel(self, x1, x2):
    if self._kernel == 'linear':
        return sum([x1[k] * x2[k] for k in range(self.n)])
    elif self._kernel == 'poly':
        return (sum([x1[k] * x2[k] for k in range(self.n)]) + 1) ** 2
    return 0
# E（x）为 g(x) 对输出 x 的预测值和 y 的差
def _E(self, i):
    return self._g(i) - self.Y[i]
def _init_alpha(self):
    # 外层循环首先遍历所有满足 0 <a<C 的样本点，测验是否满足 KKT
    index_list = [i for i in range(self.m) if 0 < self.alpha[i] < self.C]
    # 否则遍历整个训练集
    non_satisfy_list = [i for i in range(self.m) if i not in index_list]
    index_list.extend(non_satisfy_list)
    for i in index_list:
        if self._KKT(i):
            continue
        E1 = self.E[i]
        # 如果 E2 是 +，抉择最小的；如果 E2 是负的，抉择最大的
        if E1 >= 0:
            j = min(range(self.m), key=lambda x: self.E[x])
        else:
            j = max(range(self.m), key=lambda x: self.E[x])
        return i, j
def _compare(self, _alpha, L, H):
    if _alpha > H:
        return H
    elif _alpha < L:
        return L
    else:
        return _alpha
def fit(self, features, labels):
    self.init_args(features, labels)
    for t in range(self.max_iter):
        # train
        i1, i2 = self._init_alpha()
        # 边界
        if self.Y[i1] == self.Y[i2]:
            L = max(0, self.alpha[i1] + self.alpha[i2] - self.C)
            H = min(self.C, self.alpha[i1] + self.alpha[i2])
        else:
            L = max(0, self.alpha[i2] - self.alpha[i1])
            H = min(self.C, self.C + self.alpha[i2] - self.alpha[i1])
        E1 = self.E[i1]
        E2 = self.E[i2]
        # eta=K11+K22-2K12
        eta = self.kernel(self.X[i1], self.X[i1]) + self.kernel(self.X[i2],
            self.X[i2]) - 2 * self.kernel(self.X[i1], self.X[i2])
        if eta <= 0:
            # print('eta <= 0')
            continue
        alpha2_new_unc = self.alpha[i2] + self.Y[i2] * (E1 - E2) / eta  # 此处有批改，依据书上应该是 E1 - E2，书上 130-131 页
        alpha2_new = self._compare(alpha2_new_unc, L, H)
        alpha1_new =[PerfectMoney 下载](https://www.gendan5.com/wallet/PerfectMoney.html) self.alpha[i1] + self.Y[i1] * self.Y[i2] * (self.alpha[i2] - alpha2_new)
        b1_new = -E1 - self.Y[i1] * self.kernel(self.X[i1], self.X[i1]) * (alpha1_new - self.alpha[i1]) - self.Y[i2] * self.kernel(self.X[i2],
            self.X[i1]) * (alpha2_new - self.alpha[i2]) + self.b
        b2_new = -E2 - self.Y[i1] * self.kernel(self.X[i1], self.X[i2]) * (alpha1_new - self.alpha[i1]) - self.Y[i2] * self.kernel(self.X[i2],
            self.X[i2]) * (alpha2_new - self.alpha[i2]) + self.b
        if 0 < alpha1_new < self.C:
            b_new = b1_new
        elif 0 < alpha2_new < self.C:
            b_new = b2_new
        else:
            # 抉择中点
            b_new = (b1_new + b2_new) / 2
        # 更新参数
        self.alpha[i1] = alpha1_new
        self.alpha[i2] = alpha2_new
        self.b = b_new
        self.E[i1] = self._E(i1)
        self.E[i2] = self._E(i2)
    return 'train done!'
def predict(self, data):
    r = self.b
    for i in range(self.m):
        r += self.alpha[i] * self.Y[i] * self.kernel(data, self.X[i])
    return 1 if r > 0 else -1
def score(self, X_test, y_test):
    right_count = 0
    for i in range(len(X_test)):
        result = self.predict(X_test[i])
        if result == y_test[i]:
            right_count += 1
    return right_count / len(X_test)
def _weight(self):
    # linear model
    yx = self.Y.reshape(-1, 1) * self.X
    self.w = np.dot(yx.T, self.alpha)
    return self.w
def PLT(self, X, y):
    self.w = self._weight()
    plt.scatter(X[:50, 0], X[:50, 1], label="0")
    plt.scatter(X[50:, 0], X[50:, 1], label="1")
    a = -self.w[0] / self.w[1]
    xaxis = np.linspace(4, 8)
    y_sep = a * xaxis - (self.b) / self.w[1]
    plt.plot(xaxis, y_sep, 'k-')
    plt.show()

if name == ‘__main__’:

X, y = create_data()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
svm = SVM(max_iter=200)
svm.fit(X_train, y_train)
print(svm.score(X_test, y_test))
svm.PLT(X, y)