# Visualize some examples from the dataset.

# We show a few examples of training images from each class.

classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] #类别列表

num_classes = len(classes) #类别数目

samples_per_class = 7 # 每个类别采样个数

for y, cls in enumerate(classes): # 对列表的元素位置和元素进行循环，y表示元素位置（0,num_class），cls元素本身'plane'等

    idxs = np.flatnonzero(y_train == y) #找出标签中y类的位置

    idxs = np.random.choice(idxs, samples_per_class, replace=False) #从中选出我们所需的7个样本

    for i, idx in enumerate(idxs): #对所选的样本的位置和样本所对应的图片在训练集中的位置进行循环

        plt_idx = i * num_classes + y + 1 # 在子图中所占位置的计算

        plt.subplot(samples_per_class, num_classes, plt_idx) # 说明要画的子图的编号

        plt.imshow(X_train[idx].astype('uint8')) # 画图

        plt.axis('off')

        if i == 0:

            plt.title(cls) # 写上标题，也就是类别名

plt.show() # 显示

num_folds = 5

k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]

X_train_folds = []

y_train_folds = []

################################################################################

# TODO:                                                                        #

# Split up the training data into folds. After splitting, X_train_folds and    #

# y_train_folds should each be lists of length num_folds, where                #

# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #

# Hint: Look up the numpy array_split function.                                #

################################################################################

X_train_folds=np.array_split(X_train,num_folds)

y_train_folds=np.array_split(y_train,num_folds)

################################################################################

#                                 END OF YOUR CODE                             #

################################################################################

# A dictionary holding the accuracies for different values of k that we find

# when running cross-validation. After running cross-validation,

# k_to_accuracies[k] should be a list of length num_folds giving the different

# accuracy values that we found when using that value of k.

k_to_accuracies = {}

################################################################################

# TODO:                                                                        #

# Perform k-fold cross validation to find the best value of k. For each        #

# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #

# where in each case you use all but one of the folds as training data and the #

# last fold as a validation set. Store the accuracies for all fold and all     #

# values of k in the k_to_accuracies dictionary.                               #

################################################################################

num_test = X_train_folds[0].shape[0]

for j in range(len(k_choices)):

    k = k_choices[j]

    for i in range(1,num_folds+1):

        X_train_temp = np.concatenate((X_train_folds[num_folds-i],X_train_folds[num_folds-i-1],X_train_folds[num_folds-i-2],X_train_folds[num_folds-i-3]),axis = 0)

        y_train_temp = np.concatenate((y_train_folds[num_folds-i],y_train_folds[num_folds-i-1],y_train_folds[num_folds-i-2],y_train_folds[num_folds-i-3]))

        X_test_temp = X_train_folds[num_folds-i-4]

        y_test_temp = y_train_folds[num_folds-i-4]

        classifier.train(X_train_temp, y_train_temp)

        y_test_pred = classifier.predict(X_test_temp, k=k)

        num_correct = np.sum(y_test_pred == y_test_temp)

        accuracy = float(num_correct) / num_test

        k_to_accuracies.setdefault(k,[]).append(accuracy)    

################################################################################

#                                 END OF YOUR CODE                             #

################################################################################

# Print out the computed accuracies

for k in sorted(k_to_accuracies):

    for accuracy in k_to_accuracies[k]:

        print('k = %d, accuracy = %f' % (k, accuracy))