机器学习基础--KNN算法，万事开头难-1

算法设计流程图

1.建立KNN模型

import numpy as np
from math import sqrt
from collections import Counter

def kNN_classify(k, X_train, y_train, x):

    assert 1 <= k <= X_train.shape[0], "k must be valid"
    assert X_train.shape[0] == y_train.shape[0], \
        "the size of X_train must equal to the size of y_train"
    assert X_train.shape[1] == x.shape[0], \
        "the feature number of x must be equal to X_train"

    distances = [sqrt(np.sum((x_train - x)**2)) for x_train in X_train]
    nearest = np.argsort(distances)

    topK_y = [y_train[i] for i in nearest[:k]]
    votes = Counter(topK_y)

    return votes.most_common(1)[0][0]

2.初步检测模型

raw_data_X = [[3.393533211, 2.331273381],
              [3.110073483, 1.781539638],
              [1.343808831, 3.368360954],
              [3.582294042, 4.679179110],
              [2.280362439, 2.866990263],
              [7.423436942, 4.696522875],
              [5.745051997, 3.533989803],
              [9.172168622, 2.511101045],
              [7.792783481, 3.424088941],
              [7.939820817, 0.791637231]
             ]
raw_data_y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
X_train = np.array(raw_data_X)
y_train = np.array(raw_data_y)
x = np.array([8.093607318, 3.365731514])
result= kNN_classify(6,X_train,y_train,x)
result
1

3.使用scikit-learn中的kNN

from sklearn.neighbors import KNeighborsClassifier
kNN_classifier = KNeighborsClassifier(n_neighbors=6)
kNN_classifier.fit(X_train, y_train)
kNN_classifier.predict(x)
X_predict = x.reshape(1, -1)
kNN_classifier.predict(X_predict)
y_predict = kNN_classifier.predict(X_predict)
y_predict[0]
1