import shutil
import random
import numpy
import cv
from utils import cvarray
from utils.prog_bar import ProgBar
from gcp.gaussian import Gaussian
from dpgmm import DPGMM
# Tests the dpgmm model by seeing how well it fits a mixture of 2 1D Gaussians, which is visualised using open CV.
# Define the Gaussian, draw the samples...
dims = 1
gt_weight = [0.4, 0.6]
gt = [Gaussian(dims), Gaussian(dims)]
gt[0].setMean([5.0])
gt[0].setCovariance([[4.0]])
gt[1].setMean([-1.0])
gt[1].setCovariance([[4.0]])
sample_count = 2048
samples = []
for _ in xrange(sample_count):
r = random.random()
for i in xrange(len(gt)):
r -= gt_weight[i]
if r < 0.0: break
x = gt[i].sample()
samples.append(x)
import os
import shutil
import numpy
import cv
from utils import cvarray
from utils.prog_bar import ProgBar
from gcp.gaussian import Gaussian
from dpgmm import DPGMM
# Tests the dpgmm model by seeing how well it fits a single 1D Gaussian, which is visualised using open CV.
# Define the Gaussian, draw the samples...
dims = 1
gt = Gaussian(dims)
gt.setMean([5.0])
gt.setCovariance([[4.0]])
sample_count = 2048
samples = []
for _ in xrange(sample_count):
samples.append(gt.sample())
# Create the output directory...
out_dir = 'test_1d_1mode'
try:
shutil.rmtree(out_dir)
except:
pass
os.mkdir(out_dir)
