def T8():
'''
Tests if multiple ANNRs can be created without affecting each other
'''
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)
m1.fit(A, Y)
R1 = m1.GetWeightMatrix(0)
m2 = ANNR([4], [('F', 4), ('AF', 'tanh'), ('F', 1)], maxIter = 16)
m2.fit(A, Y)
R2 = m1.GetWeightMatrix(0)
if (R1 != R2).any():
return False
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
