print(newProba[11])
print(newProba[21])
print("1")
print(newProba[0:9])
print("2")
print(newProba[10:19])
print("3")
print(newProba[20:-1])
print(labels)
print(model.predict_classes(X))
from sklearn.manifold import TSNE
model = TSNE(n_components=nb_classes, random_state=0, init="pca")
toPlot = model.fit_transform(newProba)
title = "t-SNE embedding of the spectrograms"
x_min, x_max = np.min(toPlot, 0), np.max(toPlot, 0)
toPlot = (toPlot - x_min) / (x_max - x_min)
print(toPlot.shape)
labelsName = ["bob", "steve", "dave"]
cmap = sns.color_palette("Set2", n_colors=3)
plt.figure()
for i in range(toPlot.shape[0]):
plt.text(
