comparison experiment-reverb/code/supervised_training_pca_bak_2.py @ 0:246d5546657c

initial commit, needs cleanup

+#!/usr/bin/python2
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Apr 23 11:53:17 2015
+
+@author: mmxgn
+"""
+
+# This file does the cluster estimation and the removal of outliers
+
+from sys import argv, exit
+from essentia.standard import YamlInput, YamlOutput
+from essentia import Pool
+from pca import *
+
+from numpy import *
+from sklearn import cluster
+from sklearn.metrics import pairwise_distances
+from sklearn.cluster import KMeans, MiniBatchKMeans
+import matplotlib.pyplot as plt
+#from sklearn.mixture import GMM
+from sklearn.naive_bayes import GaussianNB, MultinomialNB
+from scipy.signal import decimate
+from sklearn import cross_validation
+
+#from hmmlearn import hmm
+from hmmlearn.hmm import GMM
+from hmmlearn import hmm
+#from adpcm import adm, adm_reconstruct
+
+
+mse = lambda A,B: ((array(A)-array(B)) ** 2).mean()
+
+
+
+    
+def adm_reconstruct(codeword, h, dmin=.01, dmax=.28):
+    x = zeros((1, codeword.shape[1]))
+
+    delta1 = dmin
+    delta2 = dmin
+    Sum = h
+    
+    x[0] = h
+    for i in range(0, codeword.shape[1]):
+        if codeword[0,i] == 0:
+            delta1 = dmin
+            delta2 = dmin
+            
+        elif codeword[0,i] == 1:
+            delta2 = dmin
+            Sum += delta1
+            delta1 *= 2
+            if delta1 > dmax:
+                delta1 = dmax
+                
+        elif codeword[0,i] == -1:
+            delta1 = dmin
+            Sum -= delta2
+            delta2 *= 2
+            if delta2 > dmax:
+                delta2 = dmax
+        x[0,i] = Sum
+    return x
+        
+def adm(x, dmin=.01, dmax=.28, tol=0.0001):
+    
+    # Adaptive delta modulation adapted by code:
+    # (adeltamod.m)
+    #  
+    #   Adaptive Delta Modulator
+    #   by Gandhar Desai (gdesai)
+    #   BITS Pilani Goa Campus
+    #   Date: 28 Sept, 2013     
+
+    xsig = x
+
+    Lx = len(x)
+
+    ADMout = zeros((1, Lx))        
+    codevec = zeros((1, Lx))
+    
+
+    Sum = x[0]
+    delta1 = dmin
+    delta2 = dmin
+    mult1 = 2
+    mult2 = 2
+    for i in range(0, Lx):
+        #print abs(xsig[i] - Sum)
+        if (abs(xsig[i] - Sum) < tol):
+            bit = 0
+            delta2 = dmin
+            delta1 = dmin
+            
+            
+        elif (xsig[i] >= Sum):
+            bit = 1
+            delta2 = dmin
+            Sum += delta1
+            delta1 *=  mult1
+            if delta1 > dmax:
+                delta1 = dmax
+
+
+        else:
+            bit = -1
+            delta1 = dmin            
+            Sum -= delta2
+            delta2 *= mult2
+            if delta2 > dmax:
+                delta2 = dmax
+
+        
+   
+        ADMout[0, i] = Sum
+        codevec[0, i]= bit
+        
+    return ADMout,codevec, x[0]
+
+if __name__=="__main__":
+    if len(argv) != 2:
+        print "[EE] Wrong number of arguments"
+        print "[II] Correct syntax is:"
+        print "[II] \t%s <training_file>"
+        print "[II] where <training_file> is a .yaml file containing the"
+        print "[II] features of the dataset (try output2_stage/fulltraining-last.yaml)"
+        exit(-1)
+        
+    
+    n_clusters = 4
+    UpsamplingFactor = 1
+    dmin = 0.001
+    dmax = 0.28
+    tol = 0.001    
+    
+    infile = argv[1]
+    
+    features_pool = YamlInput(filename = infile)()
+    
+    
+    
+    feature_captions = features_pool.descriptorNames()   
+    parameter_captions = []
+    
+    
+    for c in features_pool.descriptorNames():
+        if c.split('.')[0] == 'parameter':
+            parameter_captions.append(c)
+        if c.split('.')[0] == 'metadata' or c.split('.')[0] == 'parameter':
+            feature_captions.remove(c)
+            
+            
+
+    close('all')
+                
+    print "[II] Loaded training data from %s (%s) " % (infile, features_pool['metadata.date'][0])
+    print "[II] %d Features Available: " % len(feature_captions)
+
+
+    
+    print str(feature_captions).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7]
+    
+    nfeatures_in = len(feature_captions)
+    nparameters_in = len(parameter_captions)
+    features_vector = zeros((nfeatures_in, len(features_pool[feature_captions[0]])))
+    
+    parameters_vector = zeros((nparameters_in, len(features_pool[parameter_captions[0]])))
+    
+    
+    for i in range(0, nfeatures_in):
+        features_vector[i, :] = features_pool[feature_captions[i]].T
+    for i in range(0, nparameters_in):
+        parameters_vector[i, :] = features_pool[parameter_captions[0]].T
+        
+    print "[II] %d parameters used:" % len(parameter_captions)
+    print str(parameter_captions).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7].replace('parameter.','')
+    
+    print "[II] Marking silent parts"
+
+    silent_parts = zeros((1, len(features_pool[feature_captions[i]].T)))     
+
+    rms = features_vector[feature_captions.index('rms'), :]    
+    
+    # Implementing Hysteresis Gate -- High threshold is halfway between 
+    # the mean and the max and Low is halfway between the mean dn the min
+    
+    rms_threshold_mean = mean(rms)
+
+    rms_threshold_max = max(rms)
+    rms_threshold_min = min(rms)
+    
+    rms_threshold_high = 0.1 * rms_threshold_mean
+    rms_threshold_low = 0.01 * rms_threshold_mean
+    
+    for n in range(1,  len(rms)):
+        prev = rms[n-1]
+        curr = rms[n]
+        
+        if prev >= rms_threshold_high:
+            if curr < rms_threshold_low:
+                silent_parts[0,n] = 1
+            else:
+                silent_parts[0,n] = 0
+        elif prev <= rms_threshold_low:
+            if curr > rms_threshold_high:
+                silent_parts[0,n] = 0
+            else:
+                silent_parts[0,n] = 1
+        else:
+            silent_parts[0,n] = silent_parts[0,n-1]
+                
+            
+    if silent_parts[0,1] == 1:
+        silent_parts[0, 0] = 1
+        
+    
+  #  plot(rms)
+ #   plot(silent_parts.T)
+  #  plot(ones((len(rms), 1))*rms_threshold_high)
+ #   plot(ones((len(rms), 1))*rms_threshold_low)
+    
+    active_index = invert(silent_parts.flatten().astype(bool))
+    
+    # Keep only active parts
+    
+    # Uncomment this
+    features_vector = features_vector[:, active_index]
+   # parameters_vector = parameters_vector[:, active_index]
+    
+    moments_vector = zeros((features_vector.shape[0], 2))
+    
+    print "[II] Storing moments vector"
+    for i in range(0, features_vector.shape[0]):
+        mean_ = mean(features_vector[i,:])
+        std_ = std(features_vector[i,:], ddof=1)
+        moments_vector[i,0] = mean_
+        moments_vector[i,1] = std_
+        
+        features_vector[i,:] = (features_vector[i,:] - mean_)/std_
+
+    features_vector_original = features_vector
+       
+    
+    print "[II] Extracting PCA configuration "
+    
+    kernel, q, featurelist = extract_pca_configuration_from_data(features_vector)
+    
+    print "[II] Optimal number of PCs to keep: %d" % q
+    
+    feature_captions_array = array(feature_captions)
+    
+   # features_to_keep = features_vector
+    
+    features_to_keep = list(feature_captions_array[featurelist])
+    print "[II] Decided to keep %d features:" % len(features_to_keep)
+    print  str(features_to_keep).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7]
+    
+    
+    
+    # Keep the desired features
+    #Uncomment this
+    features_kept_data = features_vector[featurelist,:]
+  #  features_kept_data = features_vector
+   # features_kept_data = features_vector
+    
+    # Generate the parameter clusters using k-means
+    
+    # Uncomment this
+    features_vector = (kernel.T*features_kept_data)[0:q,:]
+    #features_vector = log(features_vector+0.001)
+  #  features_vector = features_vector_original
+    
+   
+ #   parameters_k_means = KMeans(n_clusters = parameters_k, init='k-means++', max_iter=300, tol=0.0000001, verbose = 1)
+    parameters_k_means = KMeans(init='k-means++', n_init=10, max_iter=300, tol=0.0000001, verbose = 0)
+#   #  parameters_k_means = MiniBatchKMeans(init='k-means++', max_iter=300, tol=0.00001, verbose = 1)
+#    
+#    # Quantize the differences of the parameters instead of the parameters themselves
+#    parameters_vector_diff = concatenate((zeros((shape(parameters_vector)[0],1)),diff(parameters_vector, axis=1)),axis=1)
+#    features_vector_diff = concatenate((zeros((shape(features_vector)[0],1)),diff(features_vector,axis=1)),axis=1)
+#    
+#    # Delete this afterwards
+#    # features_vector = features_vector_diff
+#    parameters_k_means.fit(parameters_vector_diff.T)
+    
+    print "[II] Trying ADM-coded parameters"
+    print "[II] Upsampling features and parameters by a factor of %d" % UpsamplingFactor
+    
+
+    # Upsampled features and parameters    
+    features_vector_upsampled = repeat(features_vector,UpsamplingFactor, axis=1)
+   # feature_labels_upsampled = repeat(features_clustering_labels,UpsamplingFactor, axis=0)
+    parameters_vector_upsampled = repeat(parameters_vector,UpsamplingFactor, axis=1)
+    
+    parameters_vector_upsampled_adm = matrix(zeros(shape(parameters_vector_upsampled)))
+    parameters_vector_upsampled_code = matrix(zeros(shape(parameters_vector_upsampled)))
+    parameters_vector_upsampled_firstval = matrix(zeros((parameters_vector_upsampled.shape[0],1)))
+    
+    # Reconstructed parameters
+    
+    parameters_vector_upsampled_reconstructed = matrix(zeros(shape(parameters_vector_upsampled)))
+    
+    
+
+    
+    def adm_matrix(X, dmin=0.001,dmax=0.28,tol=0.001):
+        
+        out = matrix(zeros(shape(X)))
+        code = matrix(zeros(shape(X)))
+        firstval = matrix(zeros((X.shape[0], 1)))
+        
+        for i in range(0, X.shape[0]):
+            out[i,:], code[i,:], firstval[i,0] = adm(X[i,:],dmin=dmin,dmax=dmax,tol=tol)
+        
+        return out,code,firstval
+        
+#        parameters_vector_upsampled_reconstructed[i,:] = adm_reconstruct(parameters_vector_upsampled_code[i,:],parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
+
+    def adm_matrix_reconstruct(code, firstval, dmin=0.001, dmax=0.28):
+        X = matrix(zeros(shape(code)))
+        for i in range(0, code.shape[0]):
+            X[i,:] = adm_reconstruct(code[i,:], firstval[i,0], dmin=dmin, dmax=dmax)
+            
+        return X
+            
+        
+    parameters_vector_upsampled_adm, parameters_vector_upsampled_code, parameters_vector_upsampled_firstval = adm_matrix(parameters_vector_upsampled, dmin, dmax, tol)
+    
+    
+    def diff_and_pad(X):
+        return concatenate((
+                zeros((
+                    shape(X)[0],
+                    1
+                )),
+                diff(X, axis=1)),
+                axis=1)
+                
+    
+    #features_vector_upsampled = diff_and_pad(diff_and_pad(features_vector_upsampled))
+    
+#    features_vector_diff = concatenate((zeros((shape(features_vector)[0],1)),diff(features_vector,axis=1)),axis=1)
+    
+    
+    
+    # Segmentation stuff
+    
+    
+    
+#    for i in range(0, parameters_vector_upsampled.shape[0]):
+#        out, code, h =  adm(parameters_vector_upsampled[i,:],dmin=dmin,dmax=dmax,tol=tol)
+#        parameters_vector_upsampled_adm[i,:] = out
+#        parameters_vector_upsampled_code[i,:] = code
+#        parameters_vector_upsampled_firstval[i, 0] = h
+       
+#    parameters_k_means.fit(parameters_vector.T)
+##    
+#    parameters_k_means_centers = parameters_k_means.cluster_centers_
+#    parameters_k_means_labels = parameters_k_means.labels_
+##    
+#    parameters_vector_estimated = zeros(shape(parameters_vector))
+##    
+#    for n in range(0, len(parameters_vector_estimated[0])):
+#        parameters_vector_estimated[:,n] = parameters_k_means_centers[parameters_k_means_labels[n]]
+##
+###    plot(parameters_vector[0])
+## #   plot(parameters_vector_estimated[0])
+##    
+##    # PROvLIMA EDW
+#    print "[II] Parameters MSE for %d clusters: %.3f" % (len(parameters_k_means.cluster_centers_), mse(parameters_vector, parameters_vector_estimated))
+#    
+##    
+    print "[II] Clustering features."
+#    
+    features_clustering = GMM(n_components = n_clusters, covariance_type='diag')
+#    
+    features_clustering.fit( features_vector_upsampled.T, y=parameters_vector_upsampled_code)
+#    
+    features_clustering_means = features_clustering.means_
+    features_clustering_labels = features_clustering.predict(features_vector_upsampled.T)
+    features_clustering_sigmas = features_clustering.covars_
+#    
+    features_vector_upsampled_estimated = zeros(shape(features_vector_upsampled))
+#    
+#
+    for n in range(0, len(features_vector_upsampled_estimated[0])):
+        features_vector_upsampled_estimated[:,n] = features_clustering_means[features_clustering_labels[n]]
+#        
+#  #  for n in range(0,features_vector.shape[0]):
+#   # hist(features_vector[1]-features_vector_estimated[1], 256)
+#    std(features_vector[1]-features_vector_estimated[1], ddof=1)
+#    mean(features_vector[1]-features_vector_estimated[1])
+#    
+    print "[II] Features MSE for %d clusters: %.3f" % (n_clusters, mse(features_vector_upsampled, features_vector_upsampled_estimated))
+    
+    
+    
+    def cross_validate_clustering(data, estimator):
+        print "[II] Crossvalidating... "
+        estimator_fulldata = estimator
+        estimator_fulldata.fit(data)
+        
+     #   labels = estimator_fulldata.predict(data)
+        means = estimator_fulldata.means_
+    #    print means
+        
+        percents = arange(0.1,0.6,0.1)
+        MSEs = []
+        reconstructed = zeros(shape(data))
+        labels = estimator.predict(data)
+        for n in range(0, len(reconstructed)):
+            reconstructed[n,:] = means[labels[n]]
+                        
+        MSEs.append(mse(data,reconstructed))
+        for p in percents:
+            train,test = cross_validation.train_test_split(data,test_size=p,random_state=0) 
+            train = matrix(train)
+            test = matrix(test)
+     #       print shape(train)
+     #       print shape(test)
+            estimator.fit(train)
+            means = estimator.means_
+            labels = estimator.predict(test)
+            reconstructed = zeros(shape(test))
+            for n in range(0, len(reconstructed)):
+                reconstructed[n,:] = means[labels[n]]
+                
+            m = mse(test,reconstructed)
+            
+            print "[II] MSE for clustering crossvalidated data %.2f-%.2f: %.5f" % ((1-p), p, m)
+            MSEs.append(m)
+            
+            print "[II] Crossvalidation complete"
+            
+        return MSEs
+        
+  #  print "[II] Trying Cross Validation"
+    
+ #   cross_validate_clustering(features_vector_upsampled.T, features_clustering)
+    
+    
+    # Construct parameters alphabet, each symbol is going to be a different column vector
+    # in parameter code matrix
+    
+    
+    def vector_to_states(X):
+        """
+        Input: a vector MxN with N samples and M variables
+        Output: a codeword dictionary `parameters_alphabet',
+        state_seq, inverse `parameters_alphabet_inv' """
+        
+                
+        parameters_alphabet = {}
+        n = 0
+        
+        for i in range(0, X.shape[1]):
+            vec = tuple(ravel(X[:,i]))
+            if vec not in parameters_alphabet:
+                parameters_alphabet[vec] = n
+                n += 1
+    
+        parameters_alphabet_inv = dict([(parameters_alphabet[m],m) for m in parameters_alphabet])
+
+        state_seq = array([parameters_alphabet[tuple(ravel(X[:,m]))] for m in range(0, parameters_vector_upsampled_code.shape[1])]    )
+        
+        return state_seq, parameters_alphabet, parameters_alphabet_inv
+        
+
+    def states_to_vector(predicted, parameters_alphabet_inv):
+        estimated = matrix(zeros((len(parameters_alphabet_inv[0]), len(predicted))))
+        for i in range(0, len(state_seq)):
+            estimated[:, i] = matrix(parameters_alphabet_inv[predicted[i]]).T       
+            
+        return estimated
+        
+    state_seq, parameters_alphabet, parameters_alphabet_inv = vector_to_states(parameters_vector_upsampled_code)
+
+    
+    parameters_change_variable = matrix(diff_and_pad(parameters_vector_upsampled)!=0).astype(int)
+    
+    changes_state_seq, changes_parameters_alphabet, changes_parameters_alphabet_inv = vector_to_states(parameters_change_variable)
+
+            
+    # This is an hmm that just codes the changes"
+    # We have only two states, change and stay the same.    
+            
+    print "[II] (changes hmm-chain) Creating emission probability mixtures for every state            "
+
+    gmm_list_changes = []
+    for n in range(0, 2):
+        vectors = features_vector_upsampled[:,changes_state_seq == n]
+        gmm = GMM(n_components=n_clusters, covariance_type = 'diag')
+        gmm.fit(vectors.T)
+        gmm_list_changes.append(gmm)
+        
+    
+    hmm_changes = hmm.GMMHMM(n_components=2, gmms=array(gmm_list_changes),n_mix=n_clusters)
+    hmm_changes.fit([array(features_vector_upsampled).T])
+
+    subplot(211)
+    plot(parameters_change_variable.T)    
+    subplot(212)
+    
+    changes_predicted_states = hmm_changes.predict(array(features_vector_upsampled.T))
+    predicted_changes_estimated = states_to_vector(changes_predicted_states, changes_parameters_alphabet_inv)
+    
+    plot(predicted_changes_estimated.T)
+
+        
+    
+    
+    # End of changes HMM here
+    
+    
+    print "[II] Creating emission probability mixtures for every state"        
+    gmm_list = []
+    for n in range(0, 3):
+        vectors = features_vector_upsampled[:,state_seq == n]
+        gmm = GMM(n_components=n_clusters,covariance_type = 'diag')
+        gmm.fit(vectors.T)
+        gmm_list.append(gmm)
+
+    hmm1 = hmm.GMMHMM(n_components=3, gmms=array(gmm_list),n_mix=n_clusters)
+    hmm1.fit([array(features_vector_upsampled).T])
+    
+    figure()
+    subplot(221)    
+    plot(parameters_vector_upsampled_code.T)
+    
+    predicted = hmm1.predict(array(features_vector_upsampled.T))
+    
+    code_estimated = matrix(zeros((len(parameters_vector_upsampled), len(state_seq))))
+    
+  #  for i in range(0, len(state_seq)):
+ #       code_estimated[:, i] = matrix(parameters_alphabet_inv[predicted[i]]).T
+    
+    code_estimated = states_to_vector(predicted,parameters_alphabet_inv)
+    subplot(222)
+    plot(code_estimated.T) 
+    
+    reconstructed_original = adm_matrix_reconstruct(parameters_vector_upsampled_code, parameters_vector_upsampled_firstval)    
+    subplot(223)
+    plot(reconstructed_original.T)
+    subplot(224)
+    reconstructed_estimated = adm_matrix_reconstruct(code_estimated, parameters_vector_upsampled_firstval)
+    plot(reconstructed_estimated.T)
+    
+#    scatter(features_vector_upsampled[0,:],features_vector_upsampled_estimated[0,:])
+#    scatter(features_vector_upsampled[1,:],features_vector_upsampled_estimated[1,:])
+#    
+#    xlabel('Original Features on Principal Components')
+#    ylabel('Estimated Features on Principal Components')
+#    title('Original vs Estimated Features')
+#    savefig('original_vs_estimated.png')
+    
+    
+        
+#        
+#        
+#    print "[II] Testing Gaussian Naive Bayes Classifier"
+##
+#    gnb = GaussianNB()    
+#    gnb.fit(features_vector_upsampled.T, parameters_vector_upsampled_code.T)
+#    parameters_vector_upsampled_code_estimated = gnb.predict(features_vector_upsampled.T)
+#    
+#    
+##    
+#    print "[II] Raw Parameters - Gaussian Naive Bayes classifier testing ratio: %.4f" % (float(sum((parameters_vector_upsampled_code_estimated == parameters_vector_upsampled_code).astype(float)))/float(len(parameters_k_means_labels)))
+##
+#    plot(adm_matrix_reconstruct(parameters_vector_upsampled_code_estimated,parameters_vector_upsampled_firstval,dmin,dmax).T)
+#    
+#    print "[II] Trying ADM-coded parameters"
+#    UpsamplingFactor = 100
+#    print "[II] Upsampling features and parameters by a factor of %d" % UpsamplingFactor
+#    
+#
+#    # Upsampled features and parameters    
+#    features_vector_upsampled = repeat(features_vector,UpsamplingFactor, axis=1)
+#    feature_labels_upsampled = repeat(features_clustering_labels,UpsamplingFactor, axis=0)
+#    parameters_vector_upsampled = repeat(parameters_vector,UpsamplingFactor, axis=1)
+#    
+#    parameters_vector_upsampled_adm = matrix(zeros(shape(parameters_vector_upsampled)))
+#    parameters_vector_upsampled_code = matrix(zeros(shape(parameters_vector_upsampled)))
+#    parameters_vector_upsampled_firstval = matrix(zeros((parameters_vector_upsampled.shape[0],1)))
+#    
+#    # Reconstructed parameters
+#    
+#    parameters_vector_upsampled_reconstructed = matrix(zeros(shape(parameters_vector_upsampled)))
+#    
+#    dmin = 0.001
+#    dmax = 0.28
+#    tol = 0.001    
+#    for i in range(0, parameters_vector_upsampled.shape[0]):
+#        out, code, h =  adm(parameters_vector_upsampled[i,:],dmin=dmin,dmax=dmax,tol=tol)
+#        parameters_vector_upsampled_adm[i,:] = out
+#        parameters_vector_upsampled_code[i,:] = code
+#        parameters_vector_upsampled_firstval[i, 0] = h
+#        
+#        
+#        # Reconstruct-ADM        
+#        parameters_vector_upsampled_reconstructed[i,:] = adm_reconstruct(parameters_vector_upsampled_code[i,:],parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
+#        
+#    
+# #   plot(parameters_vector_upsampled_adm.T, 'r--')
+#    
+#    
+#   # plot(parameters_vector_upsampled.T)
+#  #  plot(parameters_vector_upsampled_reconstructed.T, 'g.')
+#    
+#    
+#    
+#    parameters_vector_reconstructed = zeros(shape(parameters_vector))
+#    for n in range(0, parameters_vector.shape[1]):
+#        parameters_vector_reconstructed[:,n] = parameters_vector_upsampled_reconstructed[:,n*UpsamplingFactor]
+#        
+#        
+#    mse_adm = mse(parameters_vector_reconstructed, parameters_vector)
+#    
+#    print "[II] Expected ADM reconstruction MSE: %.4f" % mse_adm
+#    
+#   # figure()
+#    #plot(parameters_vector.T)
+#   # plot(parameters_vector_reconstructed.T)
+#    
+#    print "[II] Building HMM transition, emission matrices and priors"
+#    
+#    transmat = zeros((3,3))
+#    startprob = zeros((3,))
+#    emissionmat = zeros((3, n_clusters))
+#    
+#            
+#    state_labels = parameters_vector_upsampled_code + 1
+#    stateseq = state_labels.T
+#    
+#    for n in range(0,shape(parameters_vector_upsampled_code)[1]):
+#        if n>0:
+#            transmat[state_labels[0,n-1],state_labels[0,n]] += 1
+#        startprob[state_labels[0,n]] +=1
+#        emissionmat[state_labels[0,n],feature_labels_upsampled[n]] += 1
+#        
+#        
+#    for n in range(0, transmat.shape[0]):
+#        transmat[n,:]/=sum(transmat[n,:])
+#        emissionmat[n,:]/=sum(emissionmat[n,:])
+#         
+#
+#    transmat = matrix(transmat)
+#    emissionmat = matrix(emissionmat)
+#    # Prior
+#    startprob = startprob/sum(startprob)
+#    startprob = ravel(startprob)
+#    
+#    # Vocabulary
+#        
+#    model = hmm.GMMHMM(n_mix=n_clusters, n_components=3, covariance_type="diag")
+#    model.means_ = features_clustering.means_
+#    model.covars_ = features_clustering.covars_   
+#    
+#    features_vector_array = array(features_vector)
+#    features_vector_upsampled_array=array(features_vector_upsampled)
+#    
+#    model.fit([features_vector_array.T])
+#    stateseq_estimated = model.predict(features_vector_upsampled_array.T)
+#    
+#    parameters_vector_upsampled_reconstructed_decoded = matrix(zeros(shape(parameters_vector_upsampled)))
+# 
+#
+#    plot(stateseq_estimated)
+#    plot(stateseq)
+#    
+#    code_estimated = matrix(zeros(shape(parameters_vector_upsampled)))
+#    code_estimated[0,:] = stateseq_estimated - 1
+#    
+#    
+#   
+#    parameters_vector_upsampled_reconstructed_estimated = matrix(zeros(shape(parameters_vector_upsampled)))
+#
+#    for i in range(0, parameters_vector_upsampled.shape[0]):
+#        parameters_vector_upsampled_reconstructed_estimated[i,:] = adm_reconstruct(code_estimated,parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
+#    figure()
+#    plot(parameters_vector_upsampled_reconstructed_estimated.T)
+#    plot(parameters_vector_upsampled.T)