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""" |
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import time |
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import os |
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import cv2 |
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import numpy as np |
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import pandas as pd |
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from sklearn.manifold import TSNE |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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""" |
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def upload_data(path_name, number_of_class, number_of_images): |
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X_Data = [] |
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Y_Data = [] |
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for i in range(number_of_class): |
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images = os.listdir(path_name + str(i)) |
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for j in range(number_of_images): |
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img = cv2.imread(path_name + str(i)+ '/' + images[j], 0) |
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X_Data.append(img) |
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Y_Data.append(i) |
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print("> the " + str(i) + "-th file is successfully uploaded.", end='\r') |
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return np.array(X_Data), np.array(Y_Data) |
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n_class = 33 |
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n_train = 2000 |
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x_data, y_data = upload_data('/media/etabook/etadisk1/EducFils/PFE/DATA2/train_data/', n_class, n_train) |
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x_data = x_data.astype('float32') |
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x_data = np.reshape(x_data, (x_data.shape[0], 28*28)) |
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x_data /= 255 |
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print('x_data shape:', x_data.shape) |
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print(x_data.shape[0], 'data samples') |
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"""## Convert images and label vector to a Pandas DataFrame""" |
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feat_cols = [ 'pixel'+str(i) for i in range(x_data.shape[1]) ] |
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df = pd.DataFrame(x_data,columns=feat_cols) |
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df['y'] = y_data |
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df['label'] = df['y'].apply(lambda i: str(i)) |
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x_data, y_data = None, None |
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print('Size of the dataframe: {}'.format(df.shape)) |
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df.head() |
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"""## Displaying images from the Dataframe""" |
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np.random.seed(42) |
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rndperm = np.random.permutation(df.shape[0]) |
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plt.gray() |
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fig = plt.figure( figsize=(18,12) ) |
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for i in range(0,15): |
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ax = fig.add_subplot(3,5,i+1, title="Letter: {}".format(str(df.loc[rndperm[i],'label'])) ) |
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ax.matshow(df.loc[rndperm[i],feat_cols].values.reshape((28,28)).astype(float)) |
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plt.show() |
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"""## Launch of the T-SNE algorithm |
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""" |
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N = 50000 |
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df_subset = df.loc[rndperm[:N],:].copy() |
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data_subset = df_subset[feat_cols].values |
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time_start = time.time() |
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tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300) |
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tsne_results = tsne.fit_transform(data_subset) |
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print('t-SNE done! Time elapsed: {} seconds'.format(time.time()-time_start)) |
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"""## Visualisation""" |
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df_subset['tsne-2d-one'] = tsne_results[:,0] |
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df_subset['tsne-2d-two'] = tsne_results[:,1] |
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plt.figure(figsize=(16,10)) |
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sns.scatterplot( |
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x="tsne-2d-one", y="tsne-2d-two", |
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hue="y", |
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palette=sns.color_palette("hls", 33), |
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data=df_subset, |
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legend="full", |
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alpha=0.3 |
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) |
