import torch
+
def pol_prod(a, b):
m = a[:, None] * b[None, :]
mm = m.new()
k = torch.arange(a.size(0))[:, None] + torch.arange(b.size(0))[None, :]
kk = k.new()
kk.set_(k.storage(), 0, (k.size(0), k.size(0) + k.size(1) - 1), (k.size(1) - 1, 1))
- q = (kk == torch.arange(a.size(0) + b.size(0) - 1)[None, :])
+ q = kk == torch.arange(a.size(0) + b.size(0) - 1)[None, :]
return (mm * q).sum(0)
+
+def pol_eval(a, x):
+ d = torch.arange(a.size(0))
+ return (x[:, None].pow(d[None, :]) * a[None, :]).sum(1)
+
+
def pol_prim(a):
n = torch.arange(a.size(0) + 1).float()
n[1:] = a / n[1:]
return n
+
######################################################################
-if __name__ == '__main__':
- a = torch.tensor([1., 2., 3.])
- b = torch.tensor([2., 5.])
+if __name__ == "__main__":
+ a = torch.tensor([1.0, 2.0, 3.0])
+ b = torch.tensor([2.0, 5.0])
print(pol_prod(a, b))
print(pol_prim(b))
+ print(pol_eval(a, torch.tensor([0.0, 1.0, 2.0])))