重写轮子之 kNN

# !/usr/bin/python

# -*- coding:utf-8 -*-

"""

Re-implement kNN algorithm as a practice

使用该 kNN re-implement 的前提：

    train data　的标签必须转成0,1,2,...的形式

"""

# Author: 相忠良(Zhong-Liang Xiang) <ugoood@163.com>

# Finished at July 11th, 2017

import sys

from numpy import array

import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets, cross_validation

from sklearn import neighbors

## Euclidean Distance

def euclidean(v1, v2):

    v11 = np.mat(v1)

    v22 = np.mat(v2)

    return np.sqrt((v11 - v22) * ((v11 - v22).T))[0, 0]

## Cosin Distance

def cosdis(v1, v2):

    return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))

## load data

def load_data():

    iris = datasets.load_iris()

    return cross_validation.train_test_split(iris.data, iris.target, test_size=0.25, random_state=0)

class MyKNeighborsClassifier:

    predict_label = []

    n_neighbors = 0

    X_train = []

    y_train = []

    content = [] # 中间变量

    def __init__(self, n_neighbors=20):

        self.n_neighbors = n_neighbors

        return

    def fit(self, X, y):

        self.y_train = y

        self.X_train = X

    def predict(self, X):

        for item in X:

            for sample in self.X_train:

                self.content.append(euclidean(item, sample))

            temp = []

            i = 1

            while (i <= self.n_neighbors):

                index = np.argmin(self.content)

                temp.append(y_train[index])

                self.content[index] = sys.maxint

                i += 1

            self.predict_label.append(np.argmax(np.bincount(temp)))  # 重要１,2

            self.content = []

            temp = []

        return self.predict_label

    def score(self, X, y):

        pass

## 测试用例

X_train, X_test, y_train, y_test = load_data()

cls = MyKNeighborsClassifier()

cls.fit(X_train, y_train)

mine = cls.predict(X_test)

print 'my kNN: ', mine

cls1 = neighbors.KNeighborsClassifier(n_neighbors=20, p=2)

cls1.fit(X_train, y_train)

sklearnkNN = cls1.predict(X_test)

print 'sklearn kNN: ', sklearnkNN

print mine == sklearnkNN

print mine == y_test

'''

下面是编程过程中留下的经验

'''

# 重要1: np.bincount(list)

# >>> a=[1,1,2,2,4]

# >>> print np.bincount(a)

# 结果为　[0 2 2 0 1]

# 重要2: np.argmax(list)

# 返回最大值索引

# 重要3: 标识整数最大值

# >>> import sys

# >>> sys.maxint

## kNN 小示例

# def createDataset():

#     group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])

#     labels = ['A', 'A', 'B', 'B']

#     return group, labels

#

#

# dataset, labels = createDataset()

# fig = plt.figure()

# ax = fig.add_subplot(111)

# index = 0

# testdata = [0.2, 0.2]

#

# for point in dataset:

#     if labels[index] == 'A':

#         ax.scatter(point[0], point[1], c='blue', marker='o', s=300)

#     else:

#         ax.scatter(point[0], point[1], c='red', marker='^', s=300)

#     index += 1

#

# ax.scatter(testdata[0], testdata[1], c='green', marker='^', s=300)

# plt.show()