gen_data.py

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#!/usr/bin/env python
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# examples/stochastic_gradient_descent/gen_data.py
#
# Part of Project Thrill - http://project-thrill.org
#
# Copyright (C) 2017 Alina Saalfeld <[email protected]>
#
# All rights reserved. Published under the BSD-2 license in the LICENSE file.
##########################################################################

import random

D = 1
N = 1000000
SEED = 666

minx = -100
maxx = 100

param_mu = 1
param_sig = 0.2

noise_mu = 1
noise_sig = 0.1

noise_add_sig = 10

random.seed(SEED)
params = [random.gauss(param_mu, param_sig) for x in range(0, D)]

comment = "# Params: "
comment += " ".join(map(str,params))
print(comment)

print("# D = " + str(D) + ", N = " + str(N) + ", SEED = " + str(SEED))

for i in range(0,N):
        x = [random.uniform(minx, maxx) for x in range(0,D)]
        string = " ".join(map(str,x)) + " "
        value = 0
        for d in range(0,D):
                value += x[d] * params[d]
        value *= random.gauss(noise_mu, noise_sig)
        value += random.gauss(0, noise_add_sig)
        print(string + str(value))

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