On 2/7/07, Sven Schreiber <[EMAIL PROTECTED]> wrote:
> Christopher Barker schrieb:
> > Christian wrote:
> >> when creating an ndarray from a list, how can I force the result to be
> >> 2d *and* a column vector? So in case I pass a nested list, there will be no
> >> modification of the shape and when I pass a simple list, it will be
> >> converted to a 2d column vector.
> >
> > I'm not sure I understand the specification of the problem. I would
> > think that the definition of a column vector is that it's shape is:
> >
> > (-1,1)
> >
>
> So I think what's needed is:
>
> b = array(yourlist)
> b.reshape(b.shape[0], -1)
>
> Now it seems I finally understood this business with the -1 in the
> shapes... (well it's trivial if you have the book :-)
I'd like to know what the -1 means. But first I'm trying to figure out
why there are two reshapes? Do they behave identically? The doc
strings make it look like they might not.
>> x = M.rand(3,3)
>> x.reshape?
Type: builtin_function_or_method
Base Class: <type 'builtin_function_or_method'>
String Form: <built-in method reshape of matrix object at 0xb4b41df4>
Namespace: Interactive
Docstring:
a.reshape(d1, d2, ..., dn, order='c')
Return a new array from this one. The new array must have the same number
of elements as self. Also always returns a view or raises a ValueError if
that is impossible.;
>> M.reshape?
Type: function
Base Class: <type 'function'>
String Form: <function reshape at 0xb776541c>
Namespace: Interactive
File: /usr/local/lib/python2.4/site-packages/numpy/core/fromnumeric.py
Definition: M.reshape(a, newshape, order='C')
Docstring:
Return an array that uses the data of the given array, but with a new
shape.
:Parameters:
- `a` : array
- `newshape` : shape tuple or int
The new shape should be compatible with the original shape. If an
integer, then the result will be a 1D array of that length.
- `order` : 'C' or 'FORTRAN', optional (default='C')
Whether the array data should be viewed as in C (row-major) order or
FORTRAN (column-major) order.
:Returns:
- `reshaped_array` : array
This will be a new view object if possible; otherwise, it will return
a copy.
:See also:
numpy.ndarray.reshape() is the equivalent method.
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