def find_min_max_values(self, in_file_list):
logger = logging.getLogger("acoustic_norm")
file_number = len(in_file_list)
min_value_matrix = numpy.zeros((file_number, self.feature_dimension))
max_value_matrix = numpy.zeros((file_number, self.feature_dimension))
io_funcs = BinaryIOCollection()
for i in xrange(file_number):
features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension)
temp_min = numpy.amin(features, axis = 0)
temp_max = numpy.amax(features, axis = 0)
min_value_matrix[i, ] = temp_min;
max_value_matrix[i, ] = temp_max;
self.min_vector = numpy.amin(min_value_matrix, axis = 0)
self.max_vector = numpy.amax(max_value_matrix, axis = 0)
self.min_vector = numpy.reshape(self.min_vector, (1, self.feature_dimension))
self.max_vector = numpy.reshape(self.max_vector, (1, self.feature_dimension))
# po=numpy.get_printoptions()
# numpy.set_printoptions(precision=2, threshold=20, linewidth=1000, edgeitems=4)
logger.info('across %d files found min/max values of length %d:' % (file_number,self.feature_dimension) )
logger.info(' min: %s' % self.min_vector)
logger.info(' max: %s' % self.max_vector)
# restore the print options
# numpy.set_printoptions(po)
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