Пример #1
0
        def RunGMMShogun(q):
            totalTimer = Timer()

            try:
                # Load input dataset.
                Log.Info("Loading dataset", self.verbose)
                dataPoints = np.genfromtxt(self.dataset, delimiter=',')
                dataFeat = RealFeatures(dataPoints.T)

                # Get all the parameters.
                g = re.search("-g (\d+)", options)
                n = re.search("-n (\d+)", options)
                s = re.search("-n (\d+)", options)

                g = 1 if not g else int(g.group(1))
                n = 250 if not n else int(n.group(1))

                # Create the Gaussian Mixture Model.
                model = SGMM(g)
                model.set_features(dataFeat)
                with totalTimer:
                    model.train_em(1e-9, n, 1e-9)
            except Exception as e:
                q.put(-1)
                return -1

            time = totalTimer.ElapsedTime()
            q.put(time)
            return time
Пример #2
0
    def fit_gmm(self, samples):
        """
        Runs a couple of em instances on random starting points and returns
        internal GMM representation of best instance
        """
        features = RealFeatures(samples.T)

        gmms = []
        log_likelihoods = zeros(self.num_runs_em)
        for i in range(self.num_runs_em):
            # set up Shogun's GMM class and run em (corresponds to random
            # initialisation)
            gmm = GMM(self.num_components)
            gmm.set_features(features)
            log_likelihoods[i] = gmm.train_em()
            gmms.append(gmm)

        max_idx = log_likelihoods.argmax()

        # construct Gaussian mixture components in internal representation
        components = []
        for i in range(self.num_components):
            mu = gmms[max_idx].get_nth_mean(i)
            Sigma = gmms[max_idx].get_nth_cov(i)
            components.append(Gaussian(mu, Sigma))

        # construct a Gaussian mixture model based on the best EM run
        pie = gmms[max_idx].get_coef()
        proposal = MixtureDistribution(components[0].dimension,
                                       self.num_components, components,
                                       Discrete(pie))

        return proposal
Пример #3
0
    def fit_gmm(self, samples):
        """
        Runs a couple of em instances on random starting points and returns
        internal GMM representation of best instance
        """
        features = RealFeatures(samples.T)
        
        gmms = []
        log_likelihoods = zeros(self.num_runs_em)
        for i in range(self.num_runs_em):
            # set up Shogun's GMM class and run em (corresponds to random
            # initialisation)
            gmm = GMM(self.num_components)
            gmm.set_features(features)
            log_likelihoods[i] = gmm.train_em()
            gmms.append(gmm)
            
        
        max_idx = log_likelihoods.argmax()

        # construct Gaussian mixture components in internal representation
        components = []
        for i in range(self.num_components):
            mu = gmms[max_idx].get_nth_mean(i)
            Sigma = gmms[max_idx].get_nth_cov(i)
            components.append(Gaussian(mu, Sigma))
            
        # construct a Gaussian mixture model based on the best EM run
        pie = gmms[max_idx].get_coef()
        proposal = MixtureDistribution(components[0].dimension,
                                     self.num_components, components,
                                     Discrete(pie))
        
        return proposal
Пример #4
0
    def RunGMMShogun():
      totalTimer = Timer()

      try:
        # Load input dataset.
        Log.Info("Loading dataset", self.verbose)
        dataPoints = np.genfromtxt(self.dataset, delimiter=',')
        dataFeat = RealFeatures(dataPoints.T)

        # Get all the parameters.
        if "gaussians" in options:
          g = int(options.pop("gaussians"))
        else:
          Log.Fatal("Required parameter 'gaussians' not specified!")
          raise Exception("missing parameter")
        if "max_iterations" in options:
          n = int(options.pop("max_iterations"))
        else:
          n = 0

        if len(options) > 0:
          Log.Fatal("Unknown parameters: " + str(options))
          raise Exception("unknown parameters")

        # Create the Gaussian Mixture Model.
        model = SGMM(g)
        model.set_features(dataFeat)
        with totalTimer:
          model.train_em(1e-9, n, 1e-9)
      except Exception as e:
        Log.Info("Exception: " + str(e))
        return -1

      return totalTimer.ElapsedTime()
Пример #5
0
    def RunGMMShogun(q):
      totalTimer = Timer()


      try:
        # Load input dataset.
        Log.Info("Loading dataset", self.verbose)
        dataPoints = np.genfromtxt(self.dataset, delimiter=',')
        dataFeat = RealFeatures(dataPoints.T)

        # Get all the parameters.
        g = re.search("-g (\d+)", options)
        n = re.search("-n (\d+)", options)
        s = re.search("-n (\d+)", options)

        g = 1 if not g else int(g.group(1))
        n = 250 if not n else int(n.group(1))

        # Create the Gaussian Mixture Model.
        model = SGMM(g)
        model.set_features(dataFeat)
        with totalTimer:
          model.train_em(1e-9, n, 1e-9)
      except Exception as e:
        q.put(-1)
        return -1

      time = totalTimer.ElapsedTime()
      q.put(time)
      return time
Пример #6
0
        def RunGMMShogun():
            totalTimer = Timer()

            try:
                # Load input dataset.
                Log.Info("Loading dataset", self.verbose)
                dataPoints = np.genfromtxt(self.dataset, delimiter=',')
                dataFeat = RealFeatures(dataPoints.T)

                # Get all the parameters.
                if "gaussians" in options:
                    g = int(options.pop("gaussians"))
                else:
                    Log.Fatal("Required parameter 'gaussians' not specified!")
                    raise Exception("missing parameter")
                if "max_iterations" in options:
                    n = int(options.pop("max_iterations"))
                else:
                    n = 0

                if len(options) > 0:
                    Log.Fatal("Unknown parameters: " + str(options))
                    raise Exception("unknown parameters")

                # Create the Gaussian Mixture Model.
                model = SGMM(g)
                model.set_features(dataFeat)
                with totalTimer:
                    model.train_em(1e-9, n, 1e-9)
            except Exception as e:
                Log.Info("Exception: " + str(e))
                return -1

            return totalTimer.ElapsedTime()
Пример #7
0
def generate_gmm_classification_data(request):
    from modshogun import GMM, Math

    num_classes = int(request.POST['num_classes'])
    gmm = GMM(num_classes)
    total = 40
    rng = 4.0
    num = total/num_classes
    for i in xrange(num_classes):
        gmm.set_nth_mean(np.array([Math.random(-rng, rng) for j in xrange(2)]), i)
        cov_tmp = Math.normal_random(0.2, 0.1)
        cov = np.array([[1.0, cov_tmp], [cov_tmp, 1.0]], dtype=float)
        gmm.set_nth_cov(cov, i)

    data=[]
    labels=[]
    for i in xrange(num_classes):
        coef = np.zeros(num_classes)
        coef[i] = 1.0
        gmm.set_coef(coef)
        data.append(np.array([gmm.sample() for j in xrange(num)]).T)
        labels.append(np.array([i for j in xrange(num)]))

    data = np.hstack(data)
    data = data / (2.0 * rng)
    xmin = np.min(data[0,:])
    ymin = np.min(data[1,:])
    labels = np.hstack(labels)
    toy_data = []
    for i in xrange(num_classes*num):
        toy_data.append( {  'x': data[0, i] - xmin,
                            'y': data[1, i] - ymin,
                            'label': float(labels[i])})
    return HttpResponse(json.dumps(toy_data))
Пример #8
0
def generate_gmm_classification_data(request):
    from modshogun import GMM, Math

    num_classes = int(request.POST['num_classes'])
    gmm = GMM(num_classes)
    total = 40
    rng = 4.0
    num = total / num_classes
    for i in xrange(num_classes):
        gmm.set_nth_mean(np.array([Math.random(-rng, rng) for j in xrange(2)]),
                         i)
        cov_tmp = Math.normal_random(0.2, 0.1)
        cov = np.array([[1.0, cov_tmp], [cov_tmp, 1.0]], dtype=float)
        gmm.set_nth_cov(cov, i)

    data = []
    labels = []
    for i in xrange(num_classes):
        coef = np.zeros(num_classes)
        coef[i] = 1.0
        gmm.set_coef(coef)
        data.append(np.array([gmm.sample() for j in xrange(num)]).T)
        labels.append(np.array([i for j in xrange(num)]))

    data = np.hstack(data)
    data = data / (2.0 * rng)
    xmin = np.min(data[0, :])
    ymin = np.min(data[1, :])
    labels = np.hstack(labels)
    toy_data = []
    for i in xrange(num_classes * num):
        toy_data.append({
            'x': data[0, i] - xmin,
            'y': data[1, i] - ymin,
            'label': float(labels[i])
        })
    return HttpResponse(json.dumps(toy_data))
Пример #9
0
from pylab import figure,show,connect,hist,plot,legend
from numpy import array, append, arange, empty, exp
from modshogun import Gaussian, GMM
from modshogun import RealFeatures
import util

util.set_title('EM for 1d GMM example')

#set the parameters
min_cov=1e-9
max_iter=1000
min_change=1e-9

#setup the real GMM
real_gmm=GMM(3)

real_gmm.set_nth_mean(array([-2.0]), 0)
real_gmm.set_nth_mean(array([0.0]), 1)
real_gmm.set_nth_mean(array([2.0]), 2)

real_gmm.set_nth_cov(array([[0.3]]), 0)
real_gmm.set_nth_cov(array([[0.1]]), 1)
real_gmm.set_nth_cov(array([[0.2]]), 2)

real_gmm.set_coef(array([0.3, 0.5, 0.2]))

#generate training set from real GMM
generated=array([real_gmm.sample()])
for i in range(199):
    generated=append(generated, array([real_gmm.sample()]), axis=1)
Пример #10
0
from modshogun import Gaussian, GMM
from modshogun import RealFeatures
import util

util.set_title('SMEM for 2d GMM example')

#set the parameters
max_iter = 100
max_cand = 5
min_cov = 1e-9
max_em_iter = 1000
min_change = 1e-9
cov_type = 0

#setup the real GMM
real_gmm = GMM(3)

real_gmm.set_nth_mean(array([2.0, 2.0]), 0)
real_gmm.set_nth_mean(array([-2.0, -2.0]), 1)
real_gmm.set_nth_mean(array([2.0, -2.0]), 2)

real_gmm.set_nth_cov(array([[1.0, 0.2], [0.2, 0.5]]), 0)
real_gmm.set_nth_cov(array([[0.2, 0.1], [0.1, 0.5]]), 1)
real_gmm.set_nth_cov(array([[0.3, -0.2], [-0.2, 0.8]]), 2)

real_gmm.set_coef(array([0.3, 0.4, 0.3]))

#generate training set from real GMM
generated = array([real_gmm.sample()])
for i in range(199):
    generated = append(generated, array([real_gmm.sample()]), axis=0)
Пример #11
0
from numpy import array, append, arange, empty, exp
from modshogun import Gaussian, GMM
from modshogun import RealFeatures
import util

util.set_title('SMEM for 1d GMM example')

#set the parameters
max_iter = 100
max_cand = 5
min_cov = 1e-9
max_em_iter = 1000
min_change = 1e-9

#setup the real GMM
real_gmm = GMM(3)

real_gmm.set_nth_mean(array([-2.0]), 0)
real_gmm.set_nth_mean(array([0.0]), 1)
real_gmm.set_nth_mean(array([2.0]), 2)

real_gmm.set_nth_cov(array([[0.3]]), 0)
real_gmm.set_nth_cov(array([[0.1]]), 1)
real_gmm.set_nth_cov(array([[0.2]]), 2)

real_gmm.set_coef(array([0.3, 0.5, 0.2]))

#generate training set from real GMM
generated = array([real_gmm.sample()])
for i in range(199):
    generated = append(generated, array([real_gmm.sample()]), axis=1)
Пример #12
0
from pylab import figure,scatter,contour,show,legend,connect
from numpy import array, append, arange, reshape, empty, exp
from modshogun import Gaussian, GMM
from modshogun import RealFeatures
import util

util.set_title('EM for 2d GMM example')

#set the parameters
min_cov=1e-9
max_iter=1000
min_change=1e-9
cov_type=0

#setup the real GMM
real_gmm=GMM(2)

real_gmm.set_nth_mean(array([1.0, 1.0]), 0)
real_gmm.set_nth_mean(array([-1.0, -1.0]), 1)

real_gmm.set_nth_cov(array([[1.0, 0.2],[0.2, 0.1]]), 0)
real_gmm.set_nth_cov(array([[0.3, 0.1],[0.1, 1.0]]), 1)

real_gmm.set_coef(array([0.3, 0.7]))

#generate training set from real GMM
generated=array([real_gmm.sample()])
for i in range(199):
    generated=append(generated, array([real_gmm.sample()]), axis=0)

generated=generated.transpose()
Пример #13
0
from modshogun import Gaussian, GMM
from modshogun import RealFeatures
import util

util.set_title('SMEM for 2d GMM example')

#set the parameters
max_iter=100
max_cand=5
min_cov=1e-9
max_em_iter=1000
min_change=1e-9
cov_type=0

#setup the real GMM
real_gmm=GMM(3)

real_gmm.set_nth_mean(array([2.0, 2.0]), 0)
real_gmm.set_nth_mean(array([-2.0, -2.0]), 1)
real_gmm.set_nth_mean(array([2.0, -2.0]), 2)

real_gmm.set_nth_cov(array([[1.0, 0.2],[0.2, 0.5]]), 0)
real_gmm.set_nth_cov(array([[0.2, 0.1],[0.1, 0.5]]), 1)
real_gmm.set_nth_cov(array([[0.3, -0.2],[-0.2, 0.8]]), 2)

real_gmm.set_coef(array([0.3, 0.4, 0.3]))

#generate training set from real GMM
generated=array([real_gmm.sample()])
for i in range(199):
    generated=append(generated, array([real_gmm.sample()]), axis=0)
Пример #14
0
from numpy import array, append, arange, empty, exp
from modshogun import Gaussian, GMM
from modshogun import RealFeatures
import util

util.set_title('SMEM for 1d GMM example')

#set the parameters
max_iter=100
max_cand=5
min_cov=1e-9
max_em_iter=1000
min_change=1e-9

#setup the real GMM
real_gmm=GMM(3)

real_gmm.set_nth_mean(array([-2.0]), 0)
real_gmm.set_nth_mean(array([0.0]), 1)
real_gmm.set_nth_mean(array([2.0]), 2)

real_gmm.set_nth_cov(array([[0.3]]), 0)
real_gmm.set_nth_cov(array([[0.1]]), 1)
real_gmm.set_nth_cov(array([[0.2]]), 2)

real_gmm.set_coef(array([0.3, 0.5, 0.2]))

#generate training set from real GMM
generated=array([real_gmm.sample()])
for i in range(199):
    generated=append(generated, array([real_gmm.sample()]), axis=1)
Пример #15
0
from pylab import figure, show, connect, hist, plot, legend
from numpy import array, append, arange, empty, exp
from modshogun import Gaussian, GMM
from modshogun import RealFeatures
import util

util.set_title('EM for 1d GMM example')

#set the parameters
min_cov = 1e-9
max_iter = 1000
min_change = 1e-9

#setup the real GMM
real_gmm = GMM(3)

real_gmm.set_nth_mean(array([-2.0]), 0)
real_gmm.set_nth_mean(array([0.0]), 1)
real_gmm.set_nth_mean(array([2.0]), 2)

real_gmm.set_nth_cov(array([[0.3]]), 0)
real_gmm.set_nth_cov(array([[0.1]]), 1)
real_gmm.set_nth_cov(array([[0.2]]), 2)

real_gmm.set_coef(array([0.3, 0.5, 0.2]))

#generate training set from real GMM
generated = array([real_gmm.sample()])
for i in range(199):
    generated = append(generated, array([real_gmm.sample()]), axis=1)
Пример #16
0
from pylab import figure, scatter, contour, show, legend, connect
from numpy import array, append, arange, reshape, empty, exp
from modshogun import Gaussian, GMM
from modshogun import RealFeatures
import util

util.set_title('EM for 2d GMM example')

#set the parameters
min_cov = 1e-9
max_iter = 1000
min_change = 1e-9
cov_type = 0

#setup the real GMM
real_gmm = GMM(2)

real_gmm.set_nth_mean(array([1.0, 1.0]), 0)
real_gmm.set_nth_mean(array([-1.0, -1.0]), 1)

real_gmm.set_nth_cov(array([[1.0, 0.2], [0.2, 0.1]]), 0)
real_gmm.set_nth_cov(array([[0.3, 0.1], [0.1, 1.0]]), 1)

real_gmm.set_coef(array([0.3, 0.7]))

#generate training set from real GMM
generated = array([real_gmm.sample()])
for i in range(199):
    generated = append(generated, array([real_gmm.sample()]), axis=0)

generated = generated.transpose()