On Thu, Oct 6, 2011 at 7:29 AM, Samuel John wrote:
> I just learned two things:
>
> 1. np.newaxis
> 2. Array dimension broadcasting rocks more than you think.
>
>
Yup. :)
>
> The x[:, np.newaxis] might not be the most intuitive solution but it's
> great and powerful.
> Intuitive would be to h
I just learned two things:
1. np.newaxis
2. Array dimension broadcasting rocks more than you think.
The x[:, np.newaxis] might not be the most intuitive solution but it's great
and powerful.
Intuitive would be to have x.T to transform [0,1,2,4] into [[0],[1],[2],[4]].
Thanks Warren :-)
Samuel
import numpy
# Say y is
y = numpy.array([1,2,3])
Y = numpy.vstack([y,y,y,y])
# Y is array([[1, 2, 3],
# [1, 2, 3],
# [1, 2, 3],
# [1, 2, 3]])
x = numpy.array([[0],[2],[4],[6]]) # a column-vector of your scalars x0, x1...
Y - x
Hope this is what you meant.
cheers,
Sa
On Thu, Oct 6, 2011 at 7:08 AM, Neal Becker wrote:
> Given a vector y, I want a matrix H whose rows are
>
> y - x0
> y - x1
> y - x2
> ...
>
>
> where x_i are scalars
>
> Suggestion?
>
>
In [15]: import numpy as np
In [16]: y = np.array([10.0, 20.0, 30.0])
In [17]: x = np.array([0, 1, 2, 4])
Given a vector y, I want a matrix H whose rows are
y - x0
y - x1
y - x2
...
where x_i are scalars
Suggestion?
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