Source code for pygan.noisesampler.gauss_noise_sampler

# -*- coding: utf-8 -*-
import numpy as np
from pygan.noise_sampler import NoiseSampler

[docs]class GaussNoiseSampler(NoiseSampler): ''' Generate samples based on the noise prior by Gauss distribution. ''' def __init__(self, mu, sigma, output_shape): ''' Init. Args: mu: `float` or `array_like of floats`. Mean (`centre`) of the distribution. sigma: `float` or `array_like of floats`. Standard deviation (spread or `width`) of the distribution. output_shape: Output shape. the shape is `(batch size, d1, d2, d3, ...)`. ''' self.__mu = mu self.__sigma = sigma self.__output_shape = output_shape
[docs] def generate(self): ''' Generate noise samples. Returns: `np.ndarray` of samples. ''' generated_arr = np.random.normal(loc=self.__mu, scale=self.__sigma, size=self.__output_shape) if self.noise_sampler is not None: self.noise_sampler.output_shape = generated_arr.shape generated_arr += self.noise_sampler.generate() return generated_arr
[docs] def get_output_shape(self): ''' getter ''' return self.__output_shape
[docs] def set_output_shape(self, value): ''' setter ''' self.__output_shape = value
output_shape = property(get_output_shape, set_output_shape)