def test_similarity(self):
actual = Vector.similarity(self.v0, self.v1)
expected = 0.0
self.assertAlmostEqual(actual, expected)
actual = Vector.similarity(self.v1, self.v2)
expected = 70.0 / (5.477225575 * 13.190905958)
self.assertAlmostEqual(actual, expected)
def gradient(dataset: Dataset, rho: Vector) -> Vector:
grad = Vector()
for i in range(len(dataset.features)):
part_deriv = 0
for experiment in dataset:
part_deriv += (experiment.output -
rho * experiment.inputs) * experiment.inputs[i]
grad.append(-part_deriv)
return grad
def test_length(self):
actual = Vector.length(self.v0)
expected = 0.0
self.assertAlmostEqual(actual, expected)
actual = Vector.length(self.v1)
expected = 5.477225575
self.assertAlmostEqual(actual, expected)
actual = Vector.length(self.v2)
expected = 13.190905958
self.assertAlmostEqual(actual, expected)
def calculate(how_ini, branch, args, path_branch):
vector = Vector.Vector(len(args))
if how_ini == 0:
vector.initialize_with_zeros()
elif how_ini < 4:
vector.initialize_with_rd_small()
elif how_ini < 7:
vector.initialize_with_rd_medium()
else:
vector.initialize_with_rd()
if (Default.AVM_DEF == 'IteratedPatternSearch'):
pattern_search = PatternSearch.PatternSearch(Default.ACC_FACTOR_DEF)
for i in range(vector.leng):
pattern_search.setup(vector.vars, i, branch, path_branch)
next = pattern_search.evaluate()
last = ObjectiveVal.ObjectiveVal()
cnt = 0
while cnt < Default.MAX_ITERATIONS_DEF:
cnt += 1
pattern_search.initialize()
if (pattern_search.explore()):
pattern_search.pattern_move()
if (next.better_than(last) == False):
break
last = next
next = pattern_search.evaluate()
if last.best_answer():
return vector.vars
return None
def fit_linear(dataset: Dataset,
lambdaa=0.1,
max_iter=10000,
threshold=0.11) -> Tuple[Vector, float, int]:
rho = Vector(*[i for i in range(len(dataset.features))])
error = sqr_error(dataset, rho)
prev_error = 0
iter_count = 0
while iter_count < max_iter and (prev_error == 0
or abs(prev_error - error) > threshold):
rho = rho - lambdaa * gradient(dataset, rho)
prev_error, error = error, sqr_error(dataset, rho)
iter_count += 1
return rho, error, iter_count
def test_dot_product(self):
actual = Vector.dot_product(self.v1, self.v2)
expected = 5 + 12 + 21 + 32
self.assertAlmostEqual(actual, expected)