def T12():
'''
Tests saving a model to file
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis = 1) ** 2).reshape(-1, 1)
m1 = ANNR([4], [('F', 4), ('AF', 'tanh'), ('F', 1)], maxIter = 16, name = 't12ann1')
m1.fit(A, Y)
m1.SaveModel('./t12ann1')
return True
def T14():
'''
Tests saving and restore a model
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis = 1) ** 2).reshape(-1, 1)
m1 = ANNR([4], [('F', 4), ('AF', 'tanh'), ('F', 1)], maxIter = 16, name = 't12ann1')
m1.fit(A, Y)
m1.SaveModel('./t12ann1')
R1 = m1.GetWeightMatrix(0)
ANN.Reset()
m1 = ANNR([4], [('F', 4), ('AF', 'tanh'), ('F', 1)], maxIter = 16, name = 't12ann2')
m1.RestoreModel('./', 't12ann1')
R2 = m1.GetWeightMatrix(0)
if (R1 != R2).any():
return False
return True
layers = [('F', int(h)), ('AF', 'tanh'), ('F', int(h / 2)), ('AF', 'tanh'),
('F', int(h / 4)), ('AF', 'tanh'), ('F', int(h / 8)), ('AF', 'tanh'),
('F', int(h / 16)), ('AF', 'tanh'), ('F', int(h / 16)),
('AF', 'tanh'), ('F', int(h / 32)), ('AF', 'tanh'),
('F', int(h / 64)), ('AF', 'tanh'), ('F', o)]
# """
mlpr = ANNR([i],
layers,
batchSize=256,
maxIter=100000,
tol=0.05,
reg=1e-4,
verbose=True,
name='Stocker')
#Learn the data
mlpr.fit(A[0:(n - nDays)], y[0:(n - nDays)])
#save the model
mlpr.SaveModel('model/' + mlpr.name)
#Begin prediction
yHat = mlpr.predict(A)
#Plot the results
mpl.plot(A[-20:], y[-20:], c='#b0403f')
mpl.plot(A[-20:], yHat[-20:], c='#5aa9ab')
mpl.show()
mpl.plot(A, y, c='#b0403f')
mpl.plot(A, yHat, c='#5aa9ab')
mpl.show()
