Monitor.py 文件源码

def normalize_data(self, values):
        normalized_values = copy.deepcopy(values)
        data = np.array(values, dtype=float)[:, 0:5]
        data_min = np.nanmin(data, 0)
        data_max = np.nanmax(data, 0)
        print data_min
        print data_max
        for i in range(len(values)):
            for j in range(5):
                normalized_values[i][j] = np.abs(values[i][j] - data_min[j]) / np.abs(data_max[j] - data_min[j])
        return normalized_values, data_min, data_max