def predict(func, x):
'''
Predict y value using fitting function and x value.
:param func: (*Fitting function object*) Fitting function.
:param x: (*float*) x value.
:returns: (*float*) y value.
'''
if isinstance(x, (int, float, long)):
return func.predict(x)
if isinstance(x, list):
x = NDArray(ArrayUtil.array(x))
return NDArray(FittingUtil.predict(x.asarray(), func))
def expfit(x, y, func=False):
'''
Exponent fitting.
:param x: (*array_like*) x data array.
:param y: (*array_like*) y data array.
:param func: (*boolean*) Return fit function (for predict function) or not. Default is ``False``.
:returns: Fitting parameters and function (optional).
'''
if isinstance(x, list):
x = NDArray(ArrayUtil.array(x))
if isinstance(y, list):
y = NDArray(ArrayUtil.array(y))
r = FittingUtil.expFit(x.asarray(), y.asarray())
if func:
return r[0], r[1], r[2], r[3]
else:
return r[0], r[1], r[2]
def polyfit(x, y, degree, func=False):
'''
Polynomail fitting.
:param x: (*array_like*) x data array.
:param y: (*array_like*) y data array.
:param func: (*boolean*) Return fit function (for predict function) or not. Default is ``False``.
:returns: Fitting parameters and function (optional).
'''
if isinstance(x, list):
x = MIArray(ArrayUtil.array(x))
if isinstance(y, list):
y = MIArray(ArrayUtil.array(y))
r = FittingUtil.polyFit(x.asarray(), y.asarray(), degree)
if func:
return r[0], r[1], r[2]
else:
return r[0], r[1]