parameters: (images)
return_statement: return data
docstring: Allocate space for all images in one data matrix.
    The size of the data matrix is
    
    ( w  * h  * 3, numImages )
    
    where,
    
    w = width of an image in the dataset.
    h = height of an image in the dataset.
    3 is for the 3 color channels.
docstring_summary: Allocate space for all images in one data matrix.
    The size of the data matrix is
    
    ( w  * h  * 3, numImages )
    
    where,
    
    w = width of an image in the dataset.
    h = height of an image in the dataset.
    3 is for the 3 color channels.
docstring_tokens: Allocate space for all images in one data matrix . The size of the data matrix is ( w * h * 3 numImages ) where w = width of an image in the dataset . h = height of an image in the dataset . 3 is for the 3 color channels .
function: def createDataMatrix(images):
  	print("Creating data matrix",end=" ... ", flush=True)
  	''' 
  	Allocate space for all images in one data matrix.
    The size of the data matrix is
    
    ( w  * h  * 3, numImages )
    
    where,
    
    w = width of an image in the dataset.
    h = height of an image in the dataset.
    3 is for the 3 color channels.
    '''
    
  	numImages = len(images)
  	sz = images[0].shape
  	data = np.zeros((numImages, sz[0] * sz[1] * sz[2]), dtype=np.float32)
  	for i in range(0, numImages):
  		image = images[i].flatten()
  		data[i,:] = image
  	
  	print("DONE")
  	return data
function_tokens: def createDataMatrix ( images ) : print ( "Creating data matrix" , end = " ... " , flush = True ) numImages = len ( images ) sz = images [ 0 ] . shape data = np . zeros ( ( numImages , sz [ 0 ] * sz [ 1 ] * sz [ 2 ] ) , dtype = np . float32 ) for i in range ( 0 , numImages ) : image = images [ i ] . flatten ( ) data [ i , : ] = image print ( "DONE" ) return data