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 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)
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)
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from gcp.gaussian import Gaussian
from scipy.integrate import quad
import numpy
import sys
def f2(y,x,ctx):
p=max(ctx.prob([x,y]),1e-15)
return p*numpy.log(p)
def f(x,ctx):
r, abserr = quad(f2, -100, 100,args=(x,ctx))
return r
ctx = Gaussian(2)
ctx.setMean([1.0,1.0])
#一つでも対角成分が共分散より大きく低い数値だった場合エラーとなる
ctx.setCovariance([[1.0,0.5],[0.5,1.0]])
sample = []
y, abserr = quad(f, -100, 100,args=(ctx))
print -y
sys.exit(1)
for _ in range(10000):
sample.append(ctx.sample()[0])
print ctx.prob(sample[0])
#fig = plt.figure()
#ax = fig.add_subplot(111)
#! /usr/bin/env python
import os
import sys
from gcp.gaussian import Gaussian
from dpgmm import DPGMM
import numpy.random
import time
from scipy.integrate import quad
dims=2
numpy.random.seed(1);
gt = Gaussian(dims)
gt.setMean([1.0,0.0])
gt.setCovariance([[1.0,0.8],[0.8,1.0]])
sample_count = 30000
sample=[]
for _ in xrange(sample_count):
sample.append(gt.sample())
f=open('data.txt','w')
for x in sample:
f.write('%lf,%lf\n'%(x[0],x[1]))
f.close()
model = DPGMM(dims, 1)
for i,data in enumerate(sample):
model.add(data)
start = time.time()
model.setPrior()
elapsed_time = time.time() - start
num=model.solve()