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_describe(self):
data = [1, 2, 3, 4, 5, 6, 7, 8, 9]
stats = Statistics.describe(data)
self.assertEqual(stats["count"], 9)
self.assertEqual(stats["mean"], 5.0)
self.assertEqual(stats["variance"], 7.5)
self.assertEqual(stats["std"], 2.7386127875258306)
self.assertEqual(stats["min"], 1)
self.assertEqual(stats["max"], 9)
def __model_stats(self, y:[float], ycap:[float]) -> {}:
error = [0.0 for x in range(len(y))]
for i in range(len(y)):
error[i] = y[i] - ycap[i]
s = Statistics.describe(error)
return s