def _linear_regression_np(x: Series, y: Series) -> dict:
"""Simple Linear Regression in Numpy for two 1d arrays for environments without the sklearn package."""
result = {"a": npNaN, "b": npNaN, "r": npNaN, "t": npNaN, "line": npNaN}
x_sum = x.sum()
y_sum = y.sum()
if int(x_sum) != 0:
# 1st row, 2nd col value corr(x, y)
r = npCorrcoef(x, y)[0, 1]
m = x.size
r_mix = m * (x * y).sum() - x_sum * y_sum
b = r_mix // (m * (x * x).sum() - x_sum * x_sum)
a = y.mean() - b * x.mean()
line = a + b * x
_np_err = seterr()
seterr(divide="ignore", invalid="ignore")
result = {
"a": a,
"b": b,
"r": r,
"t": r / npSqrt((1 - r * r) / (m - 2)),
"line": line,
}
seterr(divide=_np_err["divide"], invalid=_np_err["invalid"])
return result
def _linear_regression_np(x: Series, y: Series) -> dict:
"""Simple Linear Regression in Numpy for two 1d arrays for environments
without the sklearn package."""
m = x.size
x_sum = x.sum()
y_sum = y.sum()
# 1st row, 2nd col value corr(x, y)
r = npCorrcoef(x, y)[0,1]
r_mixture = m * (x * y).sum() - x_sum * y_sum
b = r_mixture / (m * (x * x).sum() - x_sum * x_sum)
a = y.mean() - b * x.mean()
line = a + b * x
# seterr(divide="ignore", invalid="ignore")
return {
"a": a, "b": b, "r": r,
"t": r / npSqrt((1 - r * r) / (m - 2)),
"line": line
}