def scale(data_matrix): num_rows, num_cols = shape(data_matrix) means = [mean(get_column(data_matrix,j)) for j in range(num_cols)] stdevs = [standard_deviation(get_column(data_matrix,j)) for j in range(num_cols)] return means, stdevs
def main(): """The application entry point""" print 'EXERCISE 1A' print '===========' print 'This program takes a CSV file, asks you to select a row from that' print 'file, and then computes the mean and standard deviation of the' print 'values in that row.' print file_path = io.get_and_confirm_input('Enter csv file with values: ') data = io.read_csv_file(file_path) if not data: raise RuntimeError('No data found in file {}'.format(file_path)) column = io.choose_from_list( 'Which column would you like to use:', data[0].keys()) if column not in data[0]: raise RuntimeError('Invalid column {}'.format(column)) values = linked_list.LinkedList() for each in data: values.insert(each[column]) for each in values: print each print 'Mean: ', statistics.mean(values) print 'Std Dev: ', statistics.standard_deviation(values)
def scale(data_matrix): """вернуть средние и стандартные отклонения для каждого столбца""" num_rows, num_cols = shape(data_matrix) means = [mean(get_column(data_matrix,j)) for j in range(num_cols)] stdevs = [standard_deviation(get_column(data_matrix,j)) for j in range(num_cols)] return means, stdevs
def normalized_data(self, data): """Return the given data in normalized form. Arguments: data(list): A list of data points Returns: list: Same data points, normalized. """ mean = statistics.mean(data) stddev = statistics.standard_deviation(data) return [(each - mean)/stddev for each in data]
def get_mean(self): return statistics.mean(self.score_list)
def least_squares_fit(x, y): """при заданных обучающих значениях x и y, найти значения alpha и beta на основе МНК""" beta = correlation(x, y) * standard_deviation(y) / standard_deviation(x) alpha = mean(y) - beta * mean(x) return alpha, beta
def least_squares_fit(x, y): """given training values for x and y, find the least-squares values of alpha and beta""" beta = correlation(x, y) * standard_deviation(y) / standard_deviation(x) alpha = mean(y) - beta * mean(x) return alpha, beta
def test_should_correctly_compute_the_mean(self): self.assertAlmostEqual(5.0, statistics.mean(range(1, 10)))
def test_should_return_value_for_single_value(self): self.assertEqual(12, statistics.mean([12]))
for location, actual_language in cities: other_cities = [other_city for other_city in cities if other_city != (location, actual_language)] predicted_language = knn_classify(k, other_cities, location) if predicted_language == actual_language: num_correct += 1 print(k, "сосед(а,ей):", num_correct, "правильных из", len(cities)) dimensions = range(1, 101, 5) avg_distances = [] min_distances = [] random.seed(0) for dim in dimensions: distances = random_distances(dim, 10000) # 10000 случайных пар avg_distances.append(mean(distances)) # отследить средние расстояния min_distances.append(min(distances)) # отследить минимальные расстояния print(dim, min(distances), mean(distances), min(distances) / mean(distances)) # In[ ]: