通过调用scikit-learn 调用KNN算法进行分类

拟合的过程其实就是一个训练的过程。

使用scikit-learn封装的机器学习算法来训练数据的步骤：

1. 加载scikit-learn中机器学习所对应的算法。
   

2. 然后创建这个算法所对应的实例，如果这算法构造的过程中需要一些参数，相应的我们也需要传入一些参数。
   

3. 需要fit，拟合一次我们的数据集。
   

4. 最后就可以进行predict预测过程。
   

完整代码部分---------------------------

报错:
ValueError Traceback (most recent call last)
in
----> 1 KNN_Classifier.predict(x)

C:\Anoconda\lib\site-packages\sklearn\neighbors_classification.py in predict(self, X)
171 Class labels for each data sample.
172 “”"
–> 173 X = check_array(X, accept_sparse=‘csr’)
174
175 neigh_dist, neigh_ind = self.kneighbors(X)

C:\Anoconda\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
70 FutureWarning)
71 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
—> 72 return f(**kwargs)
73 return inner_f
74

C:\Anoconda\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
617 # If input is 1D raise error
618 if array.ndim == 1:
–> 619 raise ValueError(
620 “Expected 2D array, got 1D array instead:\narray={}.\n”
621 "Reshape your data either using array.reshape(-1, 1) if "

ValueError: Expected 2D array, got 1D array instead:
array=[8.09360732 3.36573151].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

bug解决办法：

第二部分

import numpy as np
from math import sqrt
from collections import Counterclass KNNClassifier:def __init__(self, k):""" 初始化KNN分类器 """assert k >= 1, "k must be valid"self.k = k" X_train，y_train训练数据集，以备将来在预测的时候，对于每一个新来的预测的数据" \"和训练数据集计算距离寻找前K小的这样的距离，" \"这个训练数据集在我们的类中外面的用户不能被操作，所以我把它设置未为私有的成员变量"self._X_train = Noneself._y_train = Nonedef fit(self,X_train, y_train):"""根据训练数据集X_train和y_train训练KNN分类器""""判断X_train中的样本数量和y_train中的标签数量是不是相等的"assert X_train.shape[0] == y_train.shape[0],\"the size of X_train must be equal to the size of y_train""设置K值应该是小于等于样本数(X_train)的"assert self.k <= X_train.shape[0], \"the size of X_train must be at least k."self._X_train = X_trainself._y_train = y_trainreturn selfdef _predict(self, x):"""给定单个待预测数据x,返回x_predict的预测结果值。""""X_train.shape[1]代表X_train中特征的个数。"assert x.shape[0] == self._X_train.shape[1], \"the feature number of x must be equal to X_train"distances = [sqrt(np.sum(x_train - x) ** 2) for x_train in self._X_train]"将每一个x与数据集中每一个数进行排序"nearest = np.argsort(distances)"求的小于k的数的下标，然后获取到训练标签self._y_train[i]"topK_y = [self._y_train[i] for i in nearest[:self.k]]votes = Counter(topK_y)return votes.most_common(1)[0][0]def __repr__(self):return "KNN(k=%d)" % self.kdef predict(self, X_predict):assert self._X_train is not None and self._X_train is not None,\"must fit before predict""X_predict.shape[1]代表X_predict的列"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)

调参