用python写基于knn算法对cifar-100数据集分类的程序

好的，下面是基于KNN算法对CIFAR-100数据集分类的Python程序：

首先，需要先安装必要的库，包括numpy、pickle和cPickle，可以使用以下命令进行安装：

pip install numpy

pip install pickle

pip install cPickle

接下来，我们需要加载CIFAR-100数据集，可以使用以下代码：

import numpy as np
import pickle

def unpickle(file):
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict

def load_cifar100():
    train_data = []
    train_labels = []
    test_data = []
    test_labels = []

    for i in range(1, 6):
        filename = 'cifar-100-python/train' + str(i)
        data_dict = unpickle(filename)
        train_data.append(data_dict[b'data'])
        train_labels.append(data_dict[b'fine_labels'])

    train_data = np.concatenate(train_data)
    train_labels = np.concatenate(train_labels)

    filename = 'cifar-100-python/test'
    data_dict = unpickle(filename)
    test_data = data_dict[b'data']
    test_labels = np.array(data_dict[b'fine_labels'])

    return train_data, train_labels, test_data, test_labels

接下来，我们需要对数据进行预处理，将像素值归一化到0到1之间，并将数据向量化。代码如下：

def preprocess_data(train_data, test_data):
    train_data = train_data.astype('float32')
    test_data = test_data.astype('float32')
    train_data /= 255
    test_data /= 255

    train_data = train_data.reshape(train_data.shape[0], -1)
    test_data = test_data.reshape(test_data.shape[0], -1)

    return train_data, test_data

然后，我们需要定义KNN分类器。首先，我们需要计算测试数据与训练数据之间的距离，然后选择K个最近的数据点，并将它们的标签进行统计。最后，我们选择出现最频繁的标签作为预测结果。代码如下：

from collections import Counter

class KNNClassifier:
    def __init__(self, k):
        self.k = k

    def fit(self, X, y):
        self.X_train = X
        self.y_train = y

    def predict(self, X):
        num_test = X.shape[0]
        y_pred = np.zeros(num_test, dtype = self.y_train.dtype)

        for i in range(num_test):
            distances = np.sum(np.abs(self.X_train - X[i,:]), axis=1)
            nearest_labels = self.y_train[np.argsort(distances)[:self.k]]
            c = Counter(nearest_labels)
            y_pred[i] = c.most_common(1)[0][0]

        return y_pred

最后，我们可以使用以下代码进行训练和测试：

train_data, train_labels, test_data, test_labels = load_cifar100()
train_data, test_data = preprocess_data(train_data, test_data)

knn = KNNClassifier(k=5)
knn.fit(train_data, train_labels)
y_pred = knn.predict(test_data)

accuracy = np.mean(y_pred == test_labels)
print('Accuracy:', accuracy)

这个程序会输出分类器的准确度。