def T9():
'''
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)
s1 = m1.score(A, Y)
m2 = ANNR([4], [('F', 4), ('AF', 'tanh'), ('F', 1)], maxIter = 16)
m2.fit(A, Y)
s2 = m1.score(A, Y)
if s1 != s2:
return False
return True
def T1():
'''
Tests basic functionality of ANNR
'''
A = np.random.rand(32, 4)
Y = np.random.rand(32, 1)
a = ANNR([4], [('F', 4), ('AF', 'tanh'), ('F', 1)], maxIter = 16, name = 'mlpr1')
a.fit(A, Y)
S = a.score(A, Y)
if np.isnan(S):
return False
YH = a.predict(A)
if Y.shape != YH.shape:
return False
return True
def T7():
'''
Tests basic functionality of CNNC
'''
A = np.random.rand(32, 9, 9, 3)
Y = np.random.rand(32, 1)
ws = [('C', [3, 3, 3, 4], [1, 1, 1, 1]), ('AF', 'relu'), ('P', [1, 4, 4, 1], [1, 2, 2, 1]), ('F', 16), ('AF', 'tanh'), ('F', 1)]
a = ANNR([9, 9, 3], ws, maxIter = 12, name = "cnnr1")
a.fit(A, Y)
S = a.score(A, Y)
if np.isnan(S):
return False
YH = a.predict(A)
if Y.shape != YH.shape:
return False
return True
