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helpers.py
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Mon, Feb 24, 02:04

helpers.py
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# -*- coding: utf-8 -*-
"""some helper functions."""
import numpy as np
def load_data(sub_sample=True, add_outlier=False):
"""Load data and convert it to the metrics system."""
path_dataset = "height_weight_genders.csv"
data = np.genfromtxt(
path_dataset, delimiter=",", skip_header=1, usecols=[1, 2])
height = data[:, 0]
weight = data[:, 1]
gender = np.genfromtxt(
path_dataset, delimiter=",", skip_header=1, usecols=[0],
converters={0: lambda x: 0 if b"Male" in x else 1})
# Convert to metric system
height *= 0.025
weight *= 0.454
# sub-sample
if sub_sample:
height = height[::50]
weight = weight[::50]
if add_outlier:
# outlier experiment
height = np.concatenate([height, [1.1, 1.2]])
weight = np.concatenate([weight, [51.5/0.454, 55.3/0.454]])
return height, weight, gender
def standardize(x):
"""Standardize the original data set."""
mean_x = np.mean(x)
x = x - mean_x
std_x = np.std(x)
x = x / std_x
return x, mean_x, std_x
def build_model_data(height, weight):
"""Form (y,tX) to get regression data in matrix form."""
y = weight
x = height
num_samples = len(y)
tx = np.c_[np.ones(num_samples), x]
return y, tx
def batch_iter(y, tx, batch_size, num_batches=1, shuffle=True):
"""
Generate a minibatch iterator for a dataset.
Takes as input two iterables (here the output desired values 'y' and the input data 'tx')
Outputs an iterator which gives mini-batches of `batch_size` matching elements from `y` and `tx`.
Data can be randomly shuffled to avoid ordering in the original data messing with the randomness of the minibatches.
Example of use :
for minibatch_y, minibatch_tx in batch_iter(y, tx, 32):
<DO-SOMETHING>
"""
data_size = len(y)
if shuffle:
shuffle_indices = np.random.permutation(np.arange(data_size))
shuffled_y = y[shuffle_indices]
shuffled_tx = tx[shuffle_indices]
else:
shuffled_y = y
shuffled_tx = tx
for batch_num in range(num_batches):
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size, data_size)
if start_index != end_index:
yield shuffled_y[start_index:end_index], shuffled_tx[start_index:end_index]

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