academia/machinelearning/ml_course/KNN-手动实现

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手动实现KNN

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import numpy as np
import matplotlib.pyplot as plt
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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]
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x_train = np.array(raw_data_x)
y_train = np.array(raw_data_y)
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x_train
array([[3.39353321, 2.33127338],
       [3.11007348, 1.78153964],
       [1.34380883, 3.36836095],
       [3.58229404, 4.67917911],
       [2.28036244, 2.86699026],
       [7.42343694, 4.69652288],
       [5.745052  , 3.5339898 ],
       [9.17216862, 2.51110105],
       [7.79278348, 3.42408894],
       [7.93982082, 0.79163723]])
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y_train==1
array([False, False, False, False, False,  True,  True,  True,  True,
        True])
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plt.scatter(x_train[y_train==1, 0], x_train[y_train==1, 1], color='r', label='1')
plt.scatter(x_train[y_train==0, 0], x_train[y_train==0, 1], color='g', label='0')
plt.legend()
<matplotlib.legend.Legend at 0x122311ad0>

png

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x = np.array([8.093607318, 3.365731514])

plt.scatter(x_train[y_train==1, 0], x_train[y_train==1, 1], color='r', label='1')
plt.scatter(x_train[y_train==0, 0], x_train[y_train==0, 1], color='g', label='0')
plt.scatter(x[0], x[1], color='b')
plt.legend()
plt.show()

png

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import math
distance = []
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for each in x_train:
    d = math.sqrt(np.sum((each - x) ** 2))
    distance.append(d)
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distance
[4.812566907609877,
 5.229270827235305,
 6.749798999160064,
 4.6986266144110695,
 5.83460014556857,
 1.4900114024329525,
 2.354574897431513,
 1.3761132675144652,
 0.3064319992975,
 2.5786840957478887]
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distances = [math.sqrt(np.sum((each - x) ** 2)) for each in x_train]
distances
[4.812566907609877,
 5.229270827235305,
 6.749798999160064,
 4.6986266144110695,
 5.83460014556857,
 1.4900114024329525,
 2.354574897431513,
 1.3761132675144652,
 0.3064319992975,
 2.5786840957478887]
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nearest = np.argsort(distances)
nearest
array([8, 7, 5, 6, 9, 3, 0, 1, 4, 2])
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k = 6
topK_y = [y_train[i] for i in nearest[:k]]
topK_y
[1, 1, 1, 1, 1, 0]
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import collections
votes = collections.Counter(topK_y)
votes
Counter({1: 5, 0: 1})
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votes.most_common(1)
[(1, 5)]
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predict_y = votes.most_common(1)[0][0]
predict_y
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scikit-learn实现KNN

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import numpy as np
from sklearn.neighbors import KNeighborsClassifier
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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)

target = np.array([8.093607318, 3.365731514])
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KNN_classifier = KNeighborsClassifier(n_neighbors=6)
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KNN_classifier.fit(x_train, y_train)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                     metric_params=None, n_jobs=None, n_neighbors=6, p=2,
                     weights='uniform')
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target = x.reshape(1, -1)
print(target)
[[8.09360732 3.36573151]]
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KNN_classifier.predict(target)
array([1])
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res = KNN_classifier.predict(target)
res[0]
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