phillipi

import numpy as np
import matplotlib.pyplot as plt

def mk_data(true_theta, noise_sd, N_data_points, data_mean, data_sd):
  data_x = np.expand_dims(np.random.randn(N_data_points)*data_sd + data_mean,axis=1)
  data_y = np.zeros(data_x.shape)
  for k in range(data_y.shape[0]):
    data_y[k,0] = np.expand_dims(np.array([f(data_x[k,0],true_theta)]),axis=1) + np.random.randn(1)*noise_sd
  data = np.concatenate([data_x,data_y],axis=1)
  return data

phillipi

# to be used in conjunction with the functions defined here: 
#  https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/biggan_generation_with_tf_hub.ipynb

# party parrot transformation
noise_seed_A = 3 # right facing
noise_seed_B = 31 # left facing

num_interps = 14
truncation = 0.2
category = 14