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vis_hyperspectral.py
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vis_hyperspectral.py
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from sklearn.decomposition import TruncatedSVD
from sklearn.manifold import TSNE
import matplotlib.pylab as plt
import numpy as np
import matplotlib.cm as cm
import autoencoder_sredjeno
import stacked_autoencoder
from sklearn.decomposition import PCA
def vis_data(data,classes):
X_embedded = TSNE(n_components=2, perplexity=40, verbose=2).fit_transform(data)
plt.figure()
colors = cm.rainbow(np.linspace(0, 1, 17))
for i in range(17):
ind = np.where(classes==i)
plt.scatter(X_embedded[ind,0],X_embedded[ind,1],color = colors[i],marker ='x',label = i)
plt.legend()
# Raw data
obj = AutoEncoder()
X = obj.test_set_x.get_value()
Y = obj.test_set_y.get_value()
Y = Y.argmax(axis = 1)
vis_data(X,Y)
# Autoencoder
obj.train(n_epoha=50)
X = obj.get_compressed_data(obj.test_set_x).eval()
# PCA
pca = PCA(n_components=100)
X = pca.fit_transform(obj.test_set_x.get_value())
vis_data(X,Y)
#Stacked AutoEncoder
SA = StackedAutoEncoder(n_layers=2,hidden_neurons=[100,50])
X,Y1 = sa.get_test_data()
X = X.eval()
vis_data(X,Y)