# -*- 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 metric 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 "Male" in str(x) else 1},
    )
    # Convert to metric system
    height *= 0.025
    weight *= 0.454
    return height, weight, gender


def sample_data(y, x, seed, size_samples):
    """sample from dataset."""
    np.random.seed(seed)
    num_observations = y.shape[0]
    random_permuted_indices = np.random.permutation(num_observations)
    y = y[random_permuted_indices]
    x = x[random_permuted_indices]
    return y[:size_samples], x[:size_samples]


def standardize(x):
    """Standardize the original data set."""
    mean_x = np.mean(x, axis=0)
    x = x - mean_x
    std_x = np.std(x, axis=0)
    x = x / std_x
    return x, mean_x, std_x


def de_standardize(x, mean_x, std_x):
    """Reverse the procedure of standardization."""
    x = x * std_x
    x = x + mean_x
    return 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