(X, low, high, minX=None, maxX=None, dtype=np.float)
return np.asarray(X, dtype=dtype)
min-max normalize a given matrix to given range [low,high].
    
    Args:
        X [rows x columns] input data
        low [numeric] lower bound
        high [numeric] upper bound
min-max normalize a given matrix to given range [low,high].
    
    Args:
        X [rows x columns] input data
        low [numeric] lower bound
        high [numeric] upper bound
min - max normalize a given matrix to given range [ low high ] . Args : X [ rows x columns ] input data low [ numeric ] lower bound high [ numeric ] upper bound
def minmax_normalize(X, low, high, minX=None, maxX=None, dtype=np.float):
    """ min-max normalize a given matrix to given range [low,high].
    
    Args:
        X [rows x columns] input data
        low [numeric] lower bound
        high [numeric] upper bound
    """
    if minX is None:
        minX = np.min(X)
    if maxX is None:
        maxX = np.max(X)
    minX = float(minX)
    maxX = float(maxX)
    # Normalize to [0...1].    
    X = X - minX
    X = X / (maxX - minX)
    # Scale to [low...high].
    X = X * (high-low)
    X = X + low
    return np.asarray(X, dtype=dtype)
def minmax_normalize ( X , low , high , minX = None , maxX = None , dtype = np . float ) : if minX is None : minX = np . min ( X ) if maxX is None : maxX = np . max ( X ) minX = float ( minX ) maxX = float ( maxX ) # Normalize to [0...1].     X = X - minX X = X / ( maxX - minX ) # Scale to [low...high]. X = X * ( high - low ) X = X + low return np . asarray ( X , dtype = dtype )