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I am new to Python and Numpy so maybe the title of my question is wrong.
I load some data from a matlab file
data=scipy.io.loadmat("data.mat")
x=data['x']
y=data['y']
>>> x.shape
(2194, 12276)
>>> y.shape
(2194, 1)
y is a vector and I would like to have y.shape = (2194,).
I do not the difference between (2194,) and (2194,1) but seems that sklearn.linear_model.LassoCV encounter an error if you try to load y such that y.shape=(2194,1).
So how can I change my y vector in order to have y.shape=(2194,)??
375
The NumPy ndarray class is used to represent both matrices and vectors. A vector is an array with a single dimension (there's no difference between row and column vectors), while a matrix refers to an array with two dimensions. For 3-D or higher dimensional arrays, the term tensor is also commonly used.
Conversion of a matrix into a list in Column-major order. The as. list() is an inbuilt function that takes an R language object as an argument and converts the object into a list. We have used this function to convert our matrix to a list.
If you wish to copy a matrix, then instead of using numpy. copy , use the copy method on matrix . Another alternative is to use numpy. array(x, copy=True, subok=True) .
First convert to an array, then squeeze to remove extra dimensions:
y = y.A.squeeze()
In steps:
In [217]: y = np.matrix([1,2,3]).T
In [218]: y
Out[218]:
matrix([[1],
[2],
[3]])
In [219]: y.shape
Out[219]: (3, 1)
In [220]: y = y.A
In [221]: y
Out[221]:
array([[1],
[2],
[3]])
In [222]: y.shape
Out[222]: (3, 1)
In [223]: y.squeeze()
Out[223]: array([1, 2, 3])
In [224]: y = y.squeeze()
In [225]: y.shape
Out[225]: (3,)
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