imgclsfcn

from keras.datasets import fashion_mnist
from keras.utils import to_categorical
import numpy as np

# load dataset
(trainX, trainy), (testX, testy) = fashion_mnist.load_data()
# load train and test dataset
def load_dataset():
    # load dataset
    (trainX, trainy), (testX, testY) = fashion_mnist.load_data()
    # reshape dataset to have a single channel
    trainX = trainX.reshape((trainX.shape[0], 28, 28, 1))
    testX = testX.reshape((testX.shape[0], 28, 28, 1))
    # one hot encode target values
    trainy = to_categorical(trainy)
    testY = to_categorical(testY)
    return trainX, trainy, testX, testY

seed=9

from sklearn.model_selection import StratifiedShuffleSplit

data_split = StratifiedShuffleSplit(test_size=0.08,random_state=seed)
for train_index, test_index in data_split.split(trainX, trainy):

    split_data_92, split_data_8 = trainX[train_index], trainX[test_index]

    split_label_92, split_label_8 = trainy[train_index], trainy[test_index]
train_test_split = StratifiedShuffleSplit(test_size=0.3,random_state=seed) #test_size=0.3 denotes that 30 % of the dataset is used for testing.


for train_index, test_index in train_test_split.split(split_data_8,split_label_8):

    train_data_70, test_data_30 = split_data_8[train_index], split_data_8[test_index]

    train_label_70, test_label_30 = split_label_8[train_index], split_label_8[test_index]
train_data = train_data_70 #assigning to variable train_data

train_labels = train_label_70 #assigning to variable train_labels

test_data = test_data_30

test_labels = test_label_30
print('train_data : ',train_data.shape)

print('train_labels : ',train_labels.shape)

print('test_data : ',test_data.shape)

print('test_labels : ',test_labels.shape)

# definition of normalization function

def normalize(data, eps=1e-8):

    data -= data.mean(axis=(0, 1, 2), keepdims=True)

    std = np.sqrt(data.var(axis=(0, 1, 2), ddof=1, keepdims=True))

    std[std < eps] = 1.

    data /= std

    return data
train_data=train_data.astype('float64')
test_data=test_data.astype('float64')
# calling the function

train_data = normalize(train_data)

test_data = normalize(test_data)
# prints the shape of train data and test data

print('train_data: ',train_data.shape)

print('test_data: ',test_data.shape)

# Computing whitening matrix

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

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

print('train_data_flat: ',train_data_flat.shape)

print('test_data_flat: ',test_data_flat.shape)



train_data_flat_t = train_data_flat.T

test_data_flat_t = test_data_flat.T



from sklearn.decomposition import PCA

# n_components specify the no.of components to keep

train_data_pca = PCA(n_components= 784).fit_transform(train_data_flat)

test_data_pca = PCA(n_components= 784).fit_transform(test_data_flat)

print(train_data_pca.shape)

print(test_data_pca.shape)

train_data_pca = train_data_pca.T

test_data_pca = test_data_pca.T


from skimage import color
def svdFeatures(input_data):

    svdArray_input_data=[]

    size = input_data.shape[0]

    for i in range (0,size):

        img=color.rgb2gray(input_data[i])

        U, s, V = np.linalg.svd(img, full_matrices=False);

        S=[s[i] for i in range(28)]

        svdArray_input_data.append(S)

        svdMatrix_input_data=np.matrix(svdArray_input_data)

    return svdMatrix_input_data



# apply SVD for train and test data

train_data_svd=svdFeatures(train_data)

test_data_svd=svdFeatures(test_data)
print(train_data_svd.shape)
print(test_data_svd.shape)



from sklearn import svm #Creating a svm classifier model

clf = svm.SVC(gamma=.001,probability=True) #train_data_flat_tModel training

train = clf.fit(train_data_flat_t, train_labels)
predicted= clf.predict(test_data_flat_t)


score = clf.score(test_data_flat_t,test_labels)
print("score",score)

with open('output.txt', 'w') as file:
    file.write(str(np.mean(score)))