Y = np.array([func(q) for q in qs])
Q = poly(*qs.T)
out = np.array([Y.T * q1 for q1 in Q]).T.flatten()
out = out / np.tile(val1, ns)
return out
try:
_ = _cubature
except:
raise NotImplementedError("cubature not install properly")
_cubature(f2, dim2, xmin, xmax, (), "h", abserr, relerr, norm, budget,
True, val2, err2)
shape = (dim1, ) + val.shape
val2 = val2.reshape(shape[::-1]).T
out = po.transpose(po.sum(poly * val2.T, -1))
if retall:
return out, val2, val1, err2, err1
return val2
if __name__ == "__main__":
import numpy as np
import __init__ as cp
import doctest
x, y = cp.variable(2)
doctest.testmod()
out = out/np.tile(val1, ns)
return out
try:
_ = _cubature
except:
raise NotImplementedError(
"cubature not install properly")
_cubature(f2, dim2, xmin, xmax, (), "h", abserr, relerr, norm,
budget, True, val2, err2)
shape = (dim1,)+val.shape
val2 = val2.reshape(shape[::-1]).T
out = po.transpose(po.sum(poly*val2.T, -1))
if retall:
return out, val2, val1, err2, err1
return val2
if __name__=="__main__":
import numpy as np
import __init__ as cp
import doctest
x, y = cp.variable(2)
doctest.testmod()