############################################## 2d
data, target = read_file()
data = preprocessing.scale(data)
pca = Pca()
cov = pca.cov_matrix(data[:, 0], data[:, 1], data[:, 2], data[:, 3])
values, vectors = pca.eigen_values_vectors(cov)
values, vectors = pca.sort_eigen(values, vectors)
vectors = pca.eigen_strip_vectors(values, vectors, 0.90)
print(vectors)
values = values[:len(vectors[0])]
result = np.matrix.transpose(pca.pca_result(data,
vectors)).reshape(len(data), len(data[0])-2)
result[:, 1] = -result[:, 1]
points = result
count = 0
class_points = []
fig = plt.figure()
for label in range(3):
for i in range(len(target)):
if(label == target[i]):
class_points.append(points[i])
class_points = np.array(class_points)
x = class_points[:, 0]
y = class_points[:, 1]
plt.plot(x, y, colors[label], label=str(count))
count += 1
class_points = []
labels = np.array(labels)
# # without pca
batch_size = 2
# print(inputs[0])
# leng = [len(inp) for inp in inputs]
# print(max(leng), min(leng))
# print(inputs.shape[1:])
# mlp.create_network(inputs.shape[1:], 0.001)
# mlp.train(inputs, labels, num_epochs, batch_size)
# with pca
pca = Pca()
data = preprocessing.scale(inputs)
cov = np.cov(data, rowvar=False)
values, vectors = pca.eigen_values_vectors(cov)
values, vectors = pca.sort_eigen(values, vectors)
vectors = pca.eigen_strip_vectors(values, vectors, 0.98)
print(vectors.shape)
values = values[:len(vectors[0])]
result = np.matrix.transpose(pca.pca_result(data, vectors)).reshape(
len(data), 8)
print(result.shape)
points = result
inputs = points
mlp.create_network(inputs.shape[1:], 0.001)
mlp.train(inputs, labels, num_epochs, batch_size)
