Thinking how to add tow Guessin object together? It is similar idea to Functional programming, Data type.
import math import matplotlib.pyplot as plt class Gaussian(): """ Gaussian distribution class for calculating and visualizing a Gaussian distribution. Attributes: mean (float) representing the mean value of the distribution stdev (float) representing the standard deviation of the distribution data_list (list of floats) a list of floats extracted from the data file """ def __init__(self, mu = 0, sigma = 1): self.mean = mu self.stdev = sigma self.data = [] def calculate_mean(self): """Function to calculate the mean of the data set. Args: None Returns: float: mean of the data set """ avg = 1.0 * sum(self.data) / len(self.data) self.mean = avg return self.mean def calculate_stdev(self, sample=True): """Function to calculate the standard deviation of the data set. Args: sample (bool): whether the data represents a sample or population Returns: float: standard deviation of the data set """ if sample: n = len(self.data) - 1 else: n = len(self.data) mean = self.mean sigma = 0 for d in self.data: sigma += (d - mean) ** 2 sigma = math.sqrt(sigma / n) self.stdev = sigma return self.stdev def read_data_file(self, file_name, sample=True): """Function to read in data from a txt file. The txt file should have one number (float) per line. The numbers are stored in the data attribute. After reading in the file, the mean and standard deviation are calculated Args: file_name (string): name of a file to read from Returns: None """ with open(file_name) as file: data_list = [] line = file.readline() while line: data_list.append(int(line)) line = file.readline() file.close() self.data = data_list self.mean = self.calculate_mean() self.stdev = self.calculate_stdev(sample) def plot_histogram(self): """Function to output a histogram of the instance variable data using matplotlib pyplot library. Args: None Returns: None """ plt.hist(self.data) plt.title('Histogram of Data') plt.xlabel('data') plt.ylabel('count') def pdf(self, x): """Probability density function calculator for the gaussian distribution. Args: x (float): point for calculating the probability density function Returns: float: probability density function output """ return (1.0 / (self.stdev * math.sqrt(2*math.pi))) * math.exp(-0.5*((x - self.mean) / self.stdev) ** 2) def plot_histogram_pdf(self, n_spaces = 50): """Function to plot the normalized histogram of the data and a plot of the probability density function along the same range Args: n_spaces (int): number of data points Returns: list: x values for the pdf plot list: y values for the pdf plot """ mu = self.mean sigma = self.stdev min_range = min(self.data) max_range = max(self.data) # calculates the interval between x values interval = 1.0 * (max_range - min_range) / n_spaces x = [] y = [] # calculate the x values to visualize for i in range(n_spaces): tmp = min_range + interval*i x.append(tmp) y.append(self.pdf(tmp)) # make the plots fig, axes = plt.subplots(2,sharex=True) fig.subplots_adjust(hspace=.5) axes[0].hist(self.data, density=True) axes[0].set_title('Normed Histogram of Data') axes[0].set_ylabel('Density') axes[1].plot(x, y) axes[1].set_title('Normal Distribution for \n Sample Mean and Sample Standard Deviation') axes[0].set_ylabel('Density') plt.show() return x, y def __add__(self, other): """Function to add together two Gaussian distributions Args: other (Gaussian): Gaussian instance Returns: Gaussian: Gaussian distribution """ result = Gaussian() result.mean = self.mean + other.mean result.stdev = math.sqrt(self.stdev ** 2 + other.stdev ** 2) return result def __repr__(self): """Function to output the characteristics of the Gaussian instance Args: None Returns: string: characteristics of the Gaussian """ return "mean {}, standard deviation {}".format(self.mean, self.stdev)
In the class, __in__, __add__, __repr__ are all magic methods.
import unittest class TestGaussianClass(unittest.TestCase): def setUp(self): self.gaussian = Gaussian(25, 2) def test_initialization(self): self.assertEqual(self.gaussian.mean, 25, 'incorrect mean') self.assertEqual(self.gaussian.stdev, 2, 'incorrect standard deviation') def test_pdf(self): self.assertEqual(round(self.gaussian.pdf(25), 5), 0.19947,\ 'pdf function does not give expected result') def test_meancalculation(self): self.gaussian.read_data_file('numbers.txt', True) self.assertEqual(self.gaussian.calculate_mean(),\ sum(self.gaussian.data) / float(len(self.gaussian.data)), 'calculated mean not as expected') def test_stdevcalculation(self): self.gaussian.read_data_file('numbers.txt', True) self.assertEqual(round(self.gaussian.stdev, 2), 92.87, 'sample standard deviation incorrect') self.gaussian.read_data_file('numbers.txt', False) self.assertEqual(round(self.gaussian.stdev, 2), 88.55, 'population standard deviation incorrect') def test_add(self): gaussian_one = Gaussian(25, 3) gaussian_two = Gaussian(30, 4) gaussian_sum = gaussian_one + gaussian_two self.assertEqual(gaussian_sum.mean, 55) self.assertEqual(gaussian_sum.stdev, 5) def test_repr(self): gaussian_one = Gaussian(25, 3) self.assertEqual(str(gaussian_one), "mean 25, standard deviation 3") tests = TestGaussianClass() tests_loaded = unittest.TestLoader().loadTestsFromModule(tests) unittest.TextTestRunner().run(tests_loaded)