view experiment-reverb/code/supervised_training_pca_bak3.py @ 2:c87a9505f294 tip

Added LICENSE for code, removed .wav files

#!/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 smooth_matrix_1D(X):
    window = scipy.signal.gaussian(51,4)
    window = window/sum(window)
    intermx = zeros((X.shape[0],X.shape[1]+100))
    intermx[:, 50:-50] = X
    
    for m in range(0, X.shape[0]):
       # print intermx.shape
        intermx[m,:] = convolve(ravel(intermx[m,:]), window,'same')
        
    return intermx[:,50:-50]
    
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 = 3
    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 = smooth_matrix_1D(parameters_vector_upsampled)
    
    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 = features_vector_upsampled    
    

    features_vector_upsampled = 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='full')
#    
    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)