测试代码
```
import numpy as np
from math import sqrt
from collections import Counter
#from .metrics import accuracy_score
class kNNClassifier:
def __init__(self,k):
"""初始化分类器"""
assert k>=1,"k must be valid"
self.k = k
self._X_train = None
self._y_train = None
def fit(self,X_train,y_train):
"""根据训练数据集X_train和y_train训练knn分类器kNN分类器"""
assert X_train.shape[0] == y_train.shape[0],\
"the size of X_train must be equal to the size of y_train"
assert self.k <= X_train.shape[0],\
"the size of X_train must be at least k"
self._X_train = X_train
self._y_train = y_train
return self
def predict(self,X_predict):
"""给定待遇测数据集X_predcit,返回表示X_predict结果的向量"""
assert self._X_train is not None and self._y_train is not None,\
"must fit before predict!"
assert X_predict.shape[1] == self._X_train.shape[1],\
"the feature number of X_predict must be equal to X_train"
y_predict = [self._predict(x) for x in X_predict]
return np.array(y_predict)
def _predict(self,x):
distances = [sqrt(np.sum((x_train - x)**2)) for x_train in self._X_train]
nearest = np.argsort(distances)
topK_y = [self._y_train[i] for i in nearest]
votes = Counter(topK_y)
return votes.most_common(1)[0][0]
def __repr__(self):
return "kNN(k=%d)" %self.k
```
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