机器学习——基础算法（十三）

机器学习——基础算法（十三）

一、Iris_GaussianNB

#!/usr/bin/python
# -*- coding:utf-8 -*-

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier


def iris_type(s):
    it = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}
    return (it[s])


if __name__ == "__main__":
    data = pd.read_csv('C:/Users/forever/Desktop/iris.data', header=None)
    x, y = data[np.arange(4)], data[4]
    y = pd.Categorical(values=y).codes
    feature_names = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'
    features = [0,1]#用前两项特征做分类
    x = x[features]
    x, x_test, y, y_test = train_test_split(x, y, train_size=0.7, random_state=0)

    priors = np.array((1,2,4), dtype=float)
    priors /= priors.sum()
    gnb = Pipeline([
        ('sc', MinMaxScaler()),#每一个值映射到0和1之间，然后再做分类
        ('poly', PolynomialFeatures(degree=1)),#degree值越高，分类的边扭曲程度越厉害
        ('clf', GaussianNB(priors=priors))])    # 由于鸢尾花数据是样本均衡的，其实不需要设置先验值
    # gnb = KNeighborsClassifier(n_neighbors=3).fit(x, y.ravel())
    gnb.fit(x, y.ravel())#求参数
    y_hat = gnb.predict(x)#根据求得的参数预测y值
    print ('训练集准确度: %.2f%%' % (100 * accuracy_score(y, y_hat)))#使用accuracy_score求精确度
    y_test_hat = gnb.predict(x_test)
    print ('测试集准确度：%.2f%%' % (100 * accuracy_score(y_test, y_test_hat)))  # 画图

    N, M = 500, 500     # 横纵各采样多少个值
    x1_min, x2_min = x.min()
    x1_max, x2_max = x.max()
    t1 = np.linspace(x1_min, x1_max, N)
    t2 = np.linspace(x2_min, x2_max, M)
    x1, x2 = np.meshgrid(t1, t2)                    # 生成网格采样点
    x_grid = np.stack((x1.flat, x2.flat), axis=1)   # 测试点

    mpl.rcParams['font.sans-serif'] = [u'simHei']
    mpl.rcParams['axes.unicode_minus'] = False
    cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
    cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
    y_grid_hat = gnb.predict(x_grid)                  # 预测值
    y_grid_hat = y_grid_hat.reshape(x1.shape)
    plt.figure(facecolor='w')
    plt.pcolormesh(x1, x2, y_grid_hat, cmap=cm_light)     # 预测值的显示
    plt.scatter(x[features[0]], x[features[1]], c=y, edgecolors='k', s=50, cmap=cm_dark)
    plt.scatter(x_test[features[0]], x_test[features[1]], c=y_test, marker='^', edgecolors='k', s=120, cmap=cm_dark)

    plt.xlabel(feature_names[features[0]], fontsize=13)
    plt.ylabel(feature_names[features[1]], fontsize=13)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.title(u'GaussianNB对鸢尾花数据的分类结果', fontsize=18)
    plt.grid(True)
    plt.show()

二、MarkovModel

import numpy as np
import matplotlib.pyplot as plt
import os
from matplotlib import animation
from PIL import Image


def update(f):
    global loc #loc是全局变量
    if f == 0:
        loc = loc_prime
    next_loc = np.zeros((m, n), dtype=np.float)
    for i in np.arange(m):
        for j in np.arange(n):
            next_loc[i, j] = calc_next_loc(np.array([i, j]), loc, directions)
    loc = next_loc / np.max(next_loc)#做归一化
    im.set_array(loc) #用im刷新值

    # Save
    if save_image:
        if f % 3 == 0:#保存图，每三帧做一个存储
            image_data = plt.cm.coolwarm(loc) * 255
            image_data, _ = np.split(image_data, (-1, ), axis=2)
            image_data = image_data.astype(np.uint8).clip(0, 255) #把数据存成int无符号的整型，如果这个无符号
            # 的整型比255大的切掉，用PIL做image.
            output = '.\\Pic2\\'
            if not os.path.exists(output):
                os.mkdir(output)
            a = Image.fromarray(image_data, mode='RGB')
            a.save('%s%d.png' % (output, f))
    return ([im])


def calc_next_loc(now, loc, directions):#进行loc计算方法：首先罗列出八领域，
    near_index = np.array([(-1, -1), (-1, 0), (-1, 1),
                  (0, -1), (0, 1),
                  (1, -1), (1, 0), (1, 1)])
    directions_index = np.array([7, 6, 5, 0, 4, 1, 2, 3])
    nn = now + near_index #利用当前位置加上near_index得到下一个位置的值
    ii, jj = nn[:, 0], nn[:, 1]
    ii[ii >= m] = 0#防止位置出边界
    jj[jj >= n] = 0#
    return (np.dot(loc[ii, jj], directions[ii, jj, directions_index]))


if __name__ == '__main__':
    np.set_printoptions(suppress=True, linewidth=300, edgeitems=8)
    np.random.seed(0)

    save_image = False
    style = 'Sin'   # Sin/Direct/Random
    m, n = 50, 100
    directions = np.random.rand(m, n, 8)#研究范围是50*100的区域，默认为8领域的

    if style == 'Direct':
        directions[:,:,1] = 10
    elif style == 'Sin':
        x = np.arange(n)#
        y_d = np.cos(6*np.pi*x/n)#
        theta = np.empty_like(x, dtype=np.int)#
        theta[y_d > 0.5] = 1#
        theta[~(y_d > 0.5) & (y_d > -0.5)] = 0#
        theta[~(y_d > -0.5)] = 7 #
        directions[:, x.astype(np.int), theta] = 10#
    directions[:, :] /= np.sum(directions[:, :])#
    print (directions)

    loc = np.zeros((m, n), dtype=np.float)
    loc[int(m/2), int(n/2)] = 1#把正中间位置设为初始位置
    loc_prime = np.empty_like(loc)
    loc_prime = loc#把原始位置放在loc_prime暂存
    fig = plt.figure(figsize=(8, 6), facecolor='w')
    im = plt.imshow(loc/np.max(loc), cmap='coolwarm')#loc/np.max(loc)对数据做归一化，然后显示
    anim = animation.FuncAnimation(fig, update, frames=300, interval=50, blit=True)#每50毫秒迭代一次，迭代300帧。
    #animation是可以显示动态图的类。update函数是自己写的
    plt.tight_layout(1.5)
    plt.show()