def test_covariance(self):
data_x_1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
data_y_1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
actual = (7.5, 1)
expected = Statistics.covariance(data_x_1, data_y_1)
self.assertEqual(expected, actual)
with self.assertRaises(ValueError):
Statistics.covariance([1, 2, 3], [1, 2])
data_x_1_mean = Statistics.mean(data_x_1)
data_y_1_mean = Statistics.mean(data_y_1)
expected = Statistics.covariance(data_x_1, data_y_1, data_x_1_mean,
data_y_1_mean)
self.assertEqual(expected, actual)
def pcf_data(ip, n):
op_list = []
for i in range(1, n+1):
lag_op = shift(ip, i)
op_tmp = list(Statistics.covariance(ip[i:], lag_op[i:]))
op_list.append([i, op_tmp[1]])
return op_list
def test_none_data(self):
with self.assertRaises(ValueError):
Statistics.mean(None)
with self.assertRaises(ValueError):
Statistics.mean([])
with self.assertRaises(ValueError):
Statistics.variance(None)
with self.assertRaises(ValueError):
Statistics.variance([])
with self.assertRaises(ValueError):
Statistics.covariance(None, [1])
with self.assertRaises(ValueError):
Statistics.covariance([], [1])
with self.assertRaises(ValueError):
Statistics.covariance([1], None)
with self.assertRaises(ValueError):
Statistics.covariance([1], [])
with self.assertRaises(ValueError):
Statistics.covariance([1], [1, 2])
def __stats(self, data: [[float]], y: [float]) -> []:
stats = [{} for i in range(len(data) + 1)]
y_stats = Statistics.describe(y)
(y_variance, y_std) = Statistics.variance(y, y_stats["mean"])
y_stats["covariance"] = y_variance
y_stats["r"] = 1.0
y_stats["pr"] = 1.0
stats[0] = y_stats
for i in range(len(data)):
s = Statistics.describe(data[i])
(covariance, r) = Statistics.covariance(data[i], y, s["mean"],
y_stats["mean"])
s["covariance"] = covariance
s["r"] = r
s["pr"] = self.__partial_mat__[0][i + 1]
stats[i + 1] = s
return stats
def test_partial_correlation_coefficient_greater_than_r(self):
#https://stackoverflow.com/questions/51236206/partial-correlation-values-are-larger-than-normal-correlation-in-r
hl = [7, 15, 19, 15, 21, 22, 57, 15, 20, 18]
disp = [
0.000, 0.964, 0.000, 0.000, 0.921, 0.000, 0.000, 1.006, 0.000,
1.011
]
deg = [9, 2, 3, 4, 1, 3, 1, 3, 6, 1]
BC = [
1.78e-02, 1.05e-06, 1.37e-05, 7.18e-03, 0.00e+00, 0.00e+00,
0.00e+00, 4.48e-03, 2.10e-06, 0.00e+00
]
var, r = Statistics.covariance(hl, disp)
r = round(r, 5)
self.assertEqual(r, -0.23787)
pr = Statistics.partial_correlation(hl, disp, [deg, BC])
pr = round(pr, 5)
self.assertEqual(pr, -0.67209)
mat = Statistics.partial_correlation_matrix([hl, disp, deg, BC])
mat_pr = round(mat[0][1], 5)
self.assertEqual(mat_pr, -0.67209)