def mlinregress(y, x):
'''
Implements ordinary least squares (OLS) to estimate the parameters of a multiple linear
regression model.
:param y: (*array_like*) Y sample data - one dimension array.
:param x: (*array_like*) X sample data - two dimension array.
:returns: Estimated regression parameters and residuals.
'''
if isinstance(x, list):
x = MIArray(ArrayUtil.array(x))
if isinstance(y, list):
y = MIArray(ArrayUtil.array(y))
r = StatsUtil.multipleLineRegress_OLS(y.asarray(), x.asarray())
return MIArray(r[0]), MIArray(r[1])
def mlinregress(y, x):
'''
Implements ordinary least squares (OLS) to estimate the parameters of a multiple linear
regression model.
:param y: (*array_like*) Y sample data - one dimension array.
:param x: (*array_like*) X sample data - two dimension array.
:returns: Estimated regression parameters and residuals.
'''
if isinstance(x, list):
x = MIArray(ArrayUtil.array(x))
if isinstance(y, list):
y = MIArray(ArrayUtil.array(y))
r = StatsUtil.multipleLineRegress_OLS(y.asarray(), x.asarray())
return MIArray(r[0]), MIArray(r[1])
def lstsq(a, b):
'''
Compute least-squares solution to equation Ax = b.
Compute a vector x such that the 2-norm |b - A x| is minimized.
Parameters
----------
a : (M, N) array
Left hand side matrix (2-D array).
b : (M,) array
Right hand side vector.
Returns
-------
x : (N,) array
Least-squares solution. Return shape matches shape of b.
residues : (0,) or () or (K,) ndarray
Sums of residues, squared 2-norm for each column in b - a x.
'''
r = StatsUtil.multipleLineRegress_OLS(b.asarray(), a.asarray(), True)
return NDArray(r[0]), NDArray(r[1])
def lstsq(a, b):
'''
Compute least-squares solution to equation Ax = b.
Compute a vector x such that the 2-norm |b - A x| is minimized.
Parameters
----------
a : (M, N) array
Left hand side matrix (2-D array).
b : (M,) array
Right hand side vector.
Returns
-------
x : (N,) array
Least-squares solution. Return shape matches shape of b.
residues : (0,) or () or (K,) ndarray
Sums of residues, squared 2-norm for each column in b - a x.
'''
r = StatsUtil.multipleLineRegress_OLS(b.asarray(), a.asarray(), True)
return MIArray(r[0]), MIArray(r[1])