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I have this subcode in Python and I cannot understand what it is or what it does, especially this statement:
X[:,:,:,i]
The subcode is:
train_dict = sio.loadmat(train_location)
X = np.asarray(train_dict['X'])
X_train = []
for i in range(X.shape[3]):
X_train.append(X[:,:,:,i])
X_train = np.asarray(X_train)
Y_train = train_dict['y']
for i in range(len(Y_train)):
if Y_train[i]%10 == 0:
Y_train[i] = 0
Y_train = to_categorical(Y_train,10)
return (X_train,Y_train)
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Meaning of X = X[:, 1] in Python is: X is a dataset or a array. Say Here X have n rows and n columns. so by doing x=x[:,1] we get all the rows in x present at index 1.
edited Nov 27, 2020 by hari_sh. It acts as an indicator in the format method that if you want it to be replaced by the first parameter(index zero) of format. Example : print(42+261={0}.format(303)) Here, {0} will be replaced by 303.
X is a variable name, so could be any name allowed by python for variable names. As a variable , it's value will be different every time the loop circle ends, in this particular loop range(10) the value of x will start in 0 and next 1 , and next 2, until reach value of 10.
NumPy axes are the directions along the rows and columns. Just like coordinate systems, NumPy arrays also have axes. In a 2-dimensional NumPy array, the axes are the directions along the rows and columns.
This is called array slicing. As @cᴏʟᴅsᴘᴇᴇᴅ mentioned, x is a 4D array and X[:,:,:,i] gets one specific 3D array slice of it.
Maybe an example with fewer dimensions can help.
matrix = np.arange(4).reshape((2,2))
In this case matrix is a bidimensional array:
array([[0, 1],
[2, 3]])
Therefore matrix[:, 1] will result in a smaller slice of matrix:
array([1, 3])
In original code matrix[:,:,:, 1] each of the first : mean something like "all elements in this dimension".
Have a look at how array slicing works in numpy here.
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