features_tsne = TSNE(n_components=2).fit_transform(features)
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
sns.scatterplot(features_tsne[:, 0],
features_tsne[:, 1],
hue=labels,
legend='full')
ax.set_title("T-SNE on Iris Data-Set", fontsize=16)
##################################################
print("Plotting PCA projection of data-set and classifier.")
pca = PCA()
pca.analyze(features)
pca.save("iris_results/iris")
features_compressed = pca.compress(features, 2)
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title('MLP-Classification of the Iris Data-Set', fontsize=16)
ax.set_xlim([-4.0, 4.0])
ax.set_xlabel("PCA Component 0", fontsize=12)
ax.set_ylim([-1.5, 1.5])
ax.set_ylabel("PCA Component 1", fontsize=12)
XX, YY = np.meshgrid(np.arange(*ax.get_xlim(), 0.005),
np.arange(*ax.get_ylim(), 0.005))
XY = np.vstack((XX.ravel(), YY.ravel())).T
##################################################
dimensions = [20, 100, 50, 2]
name = "faces_results/faces_model"
for d in dimensions[:-1]:
name += '_' + str(d)
print(name)
##################################################
pca = PCA()
new_pca = False
if new_pca:
eigs = pca.analyze(samples_train)
pca.save("faces_results/faces")
else:
pca.load("faces_results/faces")
samples_train_compressed = pca.compress(samples_train, dimensionality=dimensions[0])
samples_test_compressed = pca.compress(samples_test, dimensionality=dimensions[0])
##################################################
mlp = MLP(dimensions)
new_mlp = False
if new_mlp:
mlp.train(samples_train_compressed,
targets_train,
