models/non_DL_models/class_wrapper.py
def loader_to_numpy(loader):
    """
    This function turns the training and testing data
    loader information into a numpy array
    """
    g_total, s_total = None, None
    for j, (g,s) in enumerate(loader):
        if g_total is None:
            g_total = g.numpy()
        else:
            g_total = np.concatenate([g_total, g.numpy()], axis=0)
        if s_total is None:
            s_total = s.numpy()
        else:
            s_total = np.concatenate([s_total, s.numpy()], axis=0)
    print('shape of g_total is', np.shape(g_total))
    return g_total, s_total

    This function turns the training and testing data
    loader information into a numpy array
    
This function turns the training and testing data
loader information into a numpy array
This function turns the training and testing data loader information into a numpy array
def loader_to_numpy(loader):
    g_total, s_total = None, None
    for j, (g,s) in enumerate(loader):
        if g_total is None:
            g_total = g.numpy()
        else:
            g_total = np.concatenate([g_total, g.numpy()], axis=0)
        if s_total is None:
            s_total = s.numpy()
        else:
            s_total = np.concatenate([s_total, s.numpy()], axis=0)
    print('shape of g_total is', np.shape(g_total))
    return g_total, s_total
def loader_to_numpy ( loader ) : g_total , s_total = None , None for j , ( g , s ) in enumerate ( loader ) : if g_total is None : g_total = g . numpy ( ) else : g_total = np . concatenate ( [ g_total , g . numpy ( ) ] , axis = 0 ) if s_total is None : s_total = s . numpy ( ) else : s_total = np . concatenate ( [ s_total , s . numpy ( ) ] , axis = 0 ) print ( 'shape of g_total is' , np . shape ( g_total ) ) return g_total , s_total
This function turns the training and testing data
loader information into a numpy array
This function turns the training and testing data loader information into a numpy array
"""
    This function turns the training and testing data
    loader information into a numpy array
    """
loader