def think_should_have_correct_premise_values(self):
brain = Brain(["a"], [[[0.1]],[[0.1]],[[0.1]]])
with self.assertRaises(TypeError):
brain.think(0.1)
with self.assertRaises(TypeError):
brain.think(["a"])
with self.assertRaises(TypeError):
brain.think([0.1,"a"])
with self.assertRaises(ValueError):
brain.think([0.1,0.2])
def think_should_return_output(self):
brain = Brain(["a"], [[[0.1]],[[0.1]],[[0.1]]])
value = brain.think([0.1])
self.assertTrue(type(value) is float)
self.assertTrue(value < 1)
self.assertTrue(value > 0)
brain = Brain(["a"], [[[1.0]],[[0.5]],[[1.0]]])
value = brain.think([1.0])
self.assertTrue(round(value,2) == 0.35)
brain = Brain(["a"], [[[0.5],[0.5]],[[0.5,0.5],[0.5,0.5]],[[0.5,0.5]]])
value = brain.think([1.0])
self.assertTrue(round(value,2) == 0.41)
# Only one input but 2 weights for inputs
brain = Brain(["a"], [[[0.5,0.5]]])
with self.assertRaises(ValueError):
value = brain.think([1.0])
# One level network with 4 inputs
values = [
[list(float(random.uniform(0,1)) for x in range(4)), list(float(random.uniform(0,1)) for x in range(4)), list(float(random.uniform(0,1)) for x in range(4)), list(float(random.uniform(0,1)) for x in range(4))],
[list(float(random.uniform(0,1)) for x in range(4))]
]
brain = Brain(["a","b","c","d"], values)
value = brain.think(list(float(random.uniform(0,1)) for x in range(4)))
# Two level network with 4 inputs
values = [
[list(float(random.uniform(0,1)) for x in range(4)), list(float(random.uniform(0,1)) for x in range(4)), list(float(random.uniform(0,1)) for x in range(4)), list(float(random.uniform(0,1)) for x in range(4))],
[list(float(random.uniform(0,1)) for x in range(4)), list(float(random.uniform(0,1)) for x in range(4)), list(float(random.uniform(0,1)) for x in range(4)), list(float(random.uniform(0,1)) for x in range(4))],
[list(float(random.uniform(0,1)) for x in range(4))]
]
brain = Brain(["a","b","c","d"], values)
value = brain.think(list(float(random.uniform(0,1)) for x in range(4)))
print(value)
