def T1():
'''
Tests basic functionality of MLPR
'''
A = np.random.rand(32, 4)
Y = np.random.rand(32, 1)
a = MLPR([4, 4, 1], maxIter=16, name='mlpr1')
a.fit(A, Y)
a.score(A, Y)
a.predict(A)
return True
def T9():
'''
Tests if multiple MLPRs can be created without affecting each other
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis=1)**2).reshape(-1, 1)
m1 = MLPR([4, 4, 1], maxIter=16)
m1.fit(A, Y)
s1 = m1.score(A, Y)
m2 = MLPR([4, 4, 1], maxIter=16)
m2.fit(A, Y)
s2 = m1.score(A, Y)
if s1 != s2:
return False
return True