`KNN Python Realization `
```'''
k a near neighbor （kNN） The working mechanism of the algorithm is relatively simple , According to some distance measure, find out the minimum distance of a given sample k Training samples , according to k Two training samples to predict .
Classification problem ：k The category with the highest frequency is the category of the sample to be tested
The return question ： Usually, the k The average of the training samples is used as the prediction value of the sample to be tested
kNN Three elements of the model ： Distance measure 、k Choice of value 、 Classification or regression decision making
'''
import numpy as np
class KNNClassfier(object):
def __init__(self, k=5, distance='euc'):
self.k = k
self.distance = distance
self.x = None
self.y = None
def fit(self,X, Y):
'''
X : array-like [n_samples,shape]
Y : array-like [n_samples,1]
'''
self.x = X
self.y = Y
def predict(self,X_test):
'''
X_test : array-like [n_samples,shape]
Y_test : array-like [n_samples,1]
output : array-like [n_samples,1]
'''
output = np.zeros((X_test.shape[0],1))
for i in range(X_test.shape[0]):
dis = []
for j in range(self.x.shape[0]):
if self.distance == 'euc': # Euclidean distance
dis.append(np.linalg.norm(X_test[i]-self.x[j,:]))
labels = []
index=sorted(range(len(dis)), key=dis.__getitem__)
for j in range(self.k):
labels.append(self.y[index[j]])
counts = []
for label in labels:
counts.append(labels.count(label))
output[i] = labels[np.argmax(counts)]
return output
def score(self,x,y):
pred = self.predict(x)
err = 0.0
for i in range(x.shape[0]):
if pred[i]!=y[i]:
err = err+1
return 1-float(err/x.shape[0])
if __name__ == '__main__':
from sklearn import datasets
iris = datasets.load_iris()
x = iris.data
y = iris.target
# x = np.array([[0.5,0.4],[0.1,0.2],[0.7,0.8],[0.2,0.1],[0.4,0.6],[0.9,0.9],[1,1]]).reshape(-1,2)
# y = np.array([0,1,0,1,0,1,1]).reshape(-1,1)
clf = KNNClassfier(k=3)
clf.fit(x,y)
print('myknn score:',clf.score(x,y))
from sklearn.neighbors import KNeighborsClassifier
clf_sklearn = KNeighborsClassifier(n_neighbors=3)
clf_sklearn.fit(x,y)
print('sklearn score:',clf_sklearn.score(x,y))```

Handwritten digit recognition

```from sklearn import datasets
from KNN import KNNClassfier
import matplotlib.pyplot as plt
import numpy as np
import time
digits = datasets.load_digits()
x = digits.data
y = digits.target
myknn_start_time = time.time()
clf = KNNClassfier(k=5)
clf.fit(x,y)
print('myknn score:',clf.score(x,y))
myknn_end_time = time.time()
from sklearn.neighbors import KNeighborsClassifier
sklearnknn_start_time = time.time()
clf_sklearn = KNeighborsClassifier(n_neighbors=5)
clf_sklearn.fit(x,y)
print('sklearn score:',clf_sklearn.score(x,y))
sklearnknn_end_time = time.time()
print('myknn uses time:',myknn_end_time-myknn_start_time)
print('sklearn uses time:',sklearnknn_end_time-sklearnknn_start_time)```

` You can see that when dealing with large data sets , Prepared by myself kNN It's very time consuming , The reason is that every time you look up k The entire data set will be scanned when there are two neighboring points , It takes a lot of calculation , therefore k a near neighbor （kNN） We also need to consider how to find out k Nearest neighbor point , To reduce the number of distance calculations , By constructing kd Trees , Reduce searching for most points 、 Calculation ,kd The structure of the tree can be referred to 《 Statistical learning method 》- expericnce `

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