def test_sigmoid(self):
# pylint: disable=no-self-use
# pylint: disable=protected-access
"""
Tests Sigmoid Calculation.
"""
# Initialize FCORN.
fcorn5 = FCORN(window=1, rho=0.5, lambd=10)
sig = fcorn5._sigmoid(0.0)
np.testing.assert_almost_equal(sig, 0.5)
def test_fcorn_solution1(self):
"""
Test the calculation of FCORN for edge case that activation function is 0.
"""
# Initialize FCORN.
fcorn6 = FCORN(window=1, rho=0.5, lambd=10)
# Allocates asset prices to FCORN.
fcorn6.allocate(self.data, resample_by='Y')
# Create np.array of all_weights.
all_weights = np.array(fcorn6.all_weights)
# Check if all weights sum to 1.
for i in range(all_weights.shape[0]):
weights = all_weights[i]
assert (weights >= 0).all()
assert len(weights) == self.data.shape[1]
np.testing.assert_almost_equal(np.sum(weights), 1)
def test_fcorn_rho_error(self):
"""
Tests ValueError if rho is less than -1 or more than 1.
"""
# Initialize FCORN.
fcorn3 = FCORN(window=2, rho=-2, lambd=4)
with self.assertRaises(ValueError):
# Running allocate will raise ValueError.
fcorn3.allocate(self.data)
# Initialize FCORN.
fcorn4 = FCORN(window=2, rho=2, lambd=8)
with self.assertRaises(ValueError):
# Running allocate will raise ValueError.
fcorn4.allocate(self.data)
def test_fcorn_window_error(self):
"""
Tests ValueError if window is not an integer or less than 1.
"""
# Initialize FCORN.
fcorn1 = FCORN(window=2.5, rho=0.5, lambd=1)
with self.assertRaises(ValueError):
# Running allocate will raise ValueError.
fcorn1.allocate(self.data)
# Initialize FCORN.
fcorn2 = FCORN(window=0, rho=0.5, lambd=2)
with self.assertRaises(ValueError):
# Running allocate will raise ValueError.
fcorn2.allocate(self.data)
def _generate_experts(self):
"""
Generates window * rho experts from window of [1, w], rho of [0, (rho - 1) / rho], and
lambd of [1, 10 ** (lambd-1)].
"""
# Initialize expert parameters.
self.expert_params = np.zeros((self.number_of_experts, 3))
# Pointer to iterate through parameter locations.
pointer = 0
# Window from 1 to self.window.
for n_window in range(self.window):
# Rho from 0 to (rho - 1)/rho.
for n_rho in range(self.rho):
for n_lambd in range(self.lambd):
# Assign experts with parameters (n_window + 1, n_rho/rho, 10 ** n_lambd).
self.expert_params[pointer] = [
n_window + 1, n_rho / self.rho, 10**n_lambd
]
pointer += 1
for exp in range(self.number_of_experts):
param = self.expert_params[exp]
self.experts.append(FCORN(int(param[0]), param[1], param[2]))