e@0: #!/usr/bin/python2
e@0: # -*- coding: utf-8 -*-
e@0: """
e@0: Created on Thu Apr 23 11:53:17 2015
e@0: 
e@0: @author: mmxgn
e@0: """
e@0: 
e@0: # This file does the cluster estimation and the removal of outliers
e@0: 
e@0: from sys import argv, exit
e@0: from essentia.standard import YamlInput, YamlOutput
e@0: from essentia import Pool
e@0: from pca import *
e@0: 
e@0: from numpy import *
e@0: from sklearn import cluster
e@0: from sklearn.metrics import pairwise_distances
e@0: from sklearn.cluster import KMeans, MiniBatchKMeans
e@0: import matplotlib.pyplot as plt
e@0: from sklearn.mixture import GMM
e@0: from sklearn.naive_bayes import GaussianNB, MultinomialNB
e@0: from scipy.signal import decimate
e@0: #from hmmlearn import hmm
e@0: from sklearn import hmm
e@0: #from adpcm import adm, adm_reconstruct
e@0: 
e@0: 
e@0: mse = lambda A,B: ((array(A)-array(B)) ** 2).mean()
e@0: 
e@0: 
e@0: 
e@0:     
e@0: def adm_reconstruct(codeword, h, dmin=.01, dmax=.28):
e@0:     x = zeros((1, codeword.shape[1]))
e@0: 
e@0:     delta1 = dmin
e@0:     delta2 = dmin
e@0:     Sum = h
e@0:     
e@0:     x[0] = h
e@0:     for i in range(0, codeword.shape[1]):
e@0:         if codeword[0,i] == 0:
e@0:             delta1 = dmin
e@0:             delta2 = dmin
e@0:             
e@0:         elif codeword[0,i] == 1:
e@0:             delta2 = dmin
e@0:             Sum += delta1
e@0:             delta1 *= 2
e@0:             if delta1 > dmax:
e@0:                 delta1 = dmax
e@0:                 
e@0:         elif codeword[0,i] == -1:
e@0:             delta1 = dmin
e@0:             Sum -= delta2
e@0:             delta2 *= 2
e@0:             if delta2 > dmax:
e@0:                 delta2 = dmax
e@0:         x[0,i] = Sum
e@0:     return x
e@0:         
e@0: def adm(x, dmin=.01, dmax=.28, tol=0.0001):
e@0:     
e@0:     # Adaptive delta modulation adapted by code:
e@0:     # (adeltamod.m)
e@0:     #  
e@0:     #   Adaptive Delta Modulator
e@0:     #   by Gandhar Desai (gdesai)
e@0:     #   BITS Pilani Goa Campus
e@0:     #   Date: 28 Sept, 2013     
e@0: 
e@0:     xsig = x
e@0: 
e@0:     Lx = len(x)
e@0: 
e@0:     ADMout = zeros((1, Lx))        
e@0:     codevec = zeros((1, Lx))
e@0:     
e@0: 
e@0:     Sum = x[0]
e@0:     delta1 = dmin
e@0:     delta2 = dmin
e@0:     mult1 = 2
e@0:     mult2 = 2
e@0:     for i in range(0, Lx):
e@0:         #print abs(xsig[i] - Sum)
e@0:         if (abs(xsig[i] - Sum) < tol):
e@0:             bit = 0
e@0:             delta2 = dmin
e@0:             delta1 = dmin
e@0:             
e@0:             
e@0:         elif (xsig[i] >= Sum):
e@0:             bit = 1
e@0:             delta2 = dmin
e@0:             Sum += delta1
e@0:             delta1 *=  mult1
e@0:             if delta1 > dmax:
e@0:                 delta1 = dmax
e@0: 
e@0: 
e@0:         else:
e@0:             bit = -1
e@0:             delta1 = dmin            
e@0:             Sum -= delta2
e@0:             delta2 *= mult2
e@0:             if delta2 > dmax:
e@0:                 delta2 = dmax
e@0: 
e@0:         
e@0:    
e@0:         ADMout[0, i] = Sum
e@0:         codevec[0, i]= bit
e@0:         
e@0:     return ADMout,codevec, x[0]
e@0: 
e@0: if __name__=="__main__":
e@0:     if len(argv) != 2:
e@0:         print "[EE] Wrong number of arguments"
e@0:         print "[II] Correct syntax is:"
e@0:         print "[II] \t%s <training_file>"
e@0:         print "[II] where <training_file> is a .yaml file containing the"
e@0:         print "[II] features of the dataset (try output2_stage/fulltraining-last.yaml)"
e@0:         exit(-1)
e@0:         
e@0:     
e@0:     infile = argv[1]
e@0:     
e@0:     features_pool = YamlInput(filename = infile)()
e@0:     
e@0:     
e@0:     
e@0:     feature_captions = features_pool.descriptorNames()   
e@0:     parameter_captions = []
e@0:     
e@0:     
e@0:     for c in features_pool.descriptorNames():
e@0:         if c.split('.')[0] == 'parameter':
e@0:             parameter_captions.append(c)
e@0:         if c.split('.')[0] == 'metadata' or c.split('.')[0] == 'parameter':
e@0:             feature_captions.remove(c)
e@0:             
e@0:             
e@0: 
e@0:     close('all')
e@0:                 
e@0:     print "[II] Loaded training data from %s (%s) " % (infile, features_pool['metadata.date'][0])
e@0:     print "[II] %d Features Available: " % len(feature_captions)
e@0: 
e@0: 
e@0:     
e@0:     print str(feature_captions).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7]
e@0:     
e@0:     nfeatures_in = len(feature_captions)
e@0:     nparameters_in = len(parameter_captions)
e@0:     features_vector = zeros((nfeatures_in, len(features_pool[feature_captions[0]])))
e@0:     
e@0:     parameters_vector = zeros((nparameters_in, len(features_pool[parameter_captions[0]])))
e@0:     
e@0:     
e@0:     for i in range(0, nfeatures_in):
e@0:         features_vector[i, :] = features_pool[feature_captions[i]].T
e@0:     for i in range(0, nparameters_in):
e@0:         parameters_vector[i, :] = features_pool[parameter_captions[0]].T
e@0:         
e@0:     print "[II] %d parameters used:" % len(parameter_captions)
e@0:     print str(parameter_captions).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7].replace('parameter.','')
e@0:     
e@0:     print "[II] Marking silent parts"
e@0: 
e@0:     silent_parts = zeros((1, len(features_pool[feature_captions[i]].T)))     
e@0: 
e@0:     rms = features_vector[feature_captions.index('rms'), :]    
e@0:     
e@0:     # Implementing Hysteresis Gate -- High threshold is halfway between 
e@0:     # the mean and the max and Low is halfway between the mean dn the min
e@0:     
e@0:     rms_threshold_mean = mean(rms)
e@0: 
e@0:     rms_threshold_max = max(rms)
e@0:     rms_threshold_min = min(rms)
e@0:     
e@0:     rms_threshold_high = 0.1 * rms_threshold_mean
e@0:     rms_threshold_low = 0.01 * rms_threshold_mean
e@0:     
e@0:     for n in range(1,  len(rms)):
e@0:         prev = rms[n-1]
e@0:         curr = rms[n]
e@0:         
e@0:         if prev >= rms_threshold_high:
e@0:             if curr < rms_threshold_low:
e@0:                 silent_parts[0,n] = 1
e@0:             else:
e@0:                 silent_parts[0,n] = 0
e@0:         elif prev <= rms_threshold_low:
e@0:             if curr > rms_threshold_high:
e@0:                 silent_parts[0,n] = 0
e@0:             else:
e@0:                 silent_parts[0,n] = 1
e@0:         else:
e@0:             silent_parts[0,n] = silent_parts[0,n-1]
e@0:                 
e@0:             
e@0:     if silent_parts[0,1] == 1:
e@0:         silent_parts[0, 0] = 1
e@0:         
e@0:     
e@0:   #  plot(rms)
e@0:  #   plot(silent_parts.T)
e@0:   #  plot(ones((len(rms), 1))*rms_threshold_high)
e@0:  #   plot(ones((len(rms), 1))*rms_threshold_low)
e@0:     
e@0:     active_index = invert(silent_parts.flatten().astype(bool))
e@0:     
e@0:     # Keep only active parts
e@0:     
e@0:     # Uncomment this
e@0:     #features_vector = features_vector[:, active_index]
e@0:    # parameters_vector = parameters_vector[:, active_index]
e@0:     
e@0:     moments_vector = zeros((features_vector.shape[0], 2))
e@0:     
e@0:     print "[II] Storing moments vector"
e@0:     for i in range(0, features_vector.shape[0]):
e@0:         mean_ = mean(features_vector[i,:])
e@0:         std_ = std(features_vector[i,:], ddof=1)
e@0:         moments_vector[i,0] = mean_
e@0:         moments_vector[i,1] = std_
e@0:         
e@0:         features_vector[i,:] = (features_vector[i,:] - mean_)/std_
e@0: 
e@0:         
e@0:     
e@0:     print "[II] Extracting PCA configuration "
e@0:     
e@0:     kernel, q, featurelist = extract_pca_configuration_from_data(features_vector)
e@0:     
e@0:     print "[II] Optimal number of PCs to keep: %d" % q
e@0:     
e@0:     feature_captions_array = array(feature_captions)
e@0:     
e@0:     features_to_keep = features_vector
e@0:     
e@0:     features_to_keep = list(feature_captions_array[featurelist])
e@0:     print "[II] Decided to keep %d features:" % len(features_to_keep)
e@0:     print  str(features_to_keep).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7]
e@0:     
e@0:     
e@0:     
e@0:     # Keep the desired features
e@0:     #Uncomment this
e@0:     #features_kept_data = features_vector[featurelist,:]
e@0:     features_lept_data = features_vector
e@0:     
e@0:     # Generate the parameter clusters using k-means
e@0:     
e@0:     # Uncomment this
e@0:     features_vector = (kernel.T*features_kept_data)[0:q,:]
e@0:     
e@0:     
e@0:    
e@0:  #   parameters_k_means = KMeans(n_clusters = parameters_k, init='k-means++', max_iter=300, tol=0.0000001, verbose = 1)
e@0:     parameters_k_means = KMeans(init='k-means++', n_init=10, max_iter=300, tol=0.0000001, verbose = 0)
e@0: #   #  parameters_k_means = MiniBatchKMeans(init='k-means++', max_iter=300, tol=0.00001, verbose = 1)
e@0: #    
e@0: #    # Quantize the differences of the parameters instead of the parameters themselves
e@0: #    parameters_vector_diff = concatenate((zeros((shape(parameters_vector)[0],1)),diff(parameters_vector, axis=1)),axis=1)
e@0: #    features_vector_diff = concatenate((zeros((shape(features_vector)[0],1)),diff(features_vector,axis=1)),axis=1)
e@0: #    
e@0: #    # Delete this afterwards
e@0: #    # features_vector = features_vector_diff
e@0: #    parameters_k_means.fit(parameters_vector_diff.T)
e@0:     parameters_k_means.fit(parameters_vector.T)
e@0: #    
e@0:     parameters_k_means_centers = parameters_k_means.cluster_centers_
e@0:     parameters_k_means_labels = parameters_k_means.labels_
e@0: #    
e@0:     parameters_vector_estimated = zeros(shape(parameters_vector))
e@0: #    
e@0:     for n in range(0, len(parameters_vector_estimated[0])):
e@0:         parameters_vector_estimated[:,n] = parameters_k_means_centers[parameters_k_means_labels[n]]
e@0: #
e@0: ##    plot(parameters_vector[0])
e@0: # #   plot(parameters_vector_estimated[0])
e@0: #    
e@0: #    # PROvLIMA EDW
e@0:     print "[II] Parameters MSE for %d clusters: %.3f" % (len(parameters_k_means.cluster_centers_), mse(parameters_vector, parameters_vector_estimated))
e@0:     
e@0: #    
e@0:     
e@0:     
e@0: 
e@0: 
e@0: 
e@0: #    print "[II] Clustering features."
e@0: #    
e@0: #    n_clusters = 100
e@0: #    features_clustering = GMM(n_components = n_clusters, covariance_type='diag')
e@0: #    
e@0: #    features_clustering.fit( features_vector.T, y=parameters_k_means_labels)
e@0: #    
e@0: #    features_clustering_means = features_clustering.means_
e@0: #    features_clustering_labels = features_clustering.predict(features_vector.T)
e@0: #    features_clustering_sigmas = features_clustering.covars_
e@0: #    
e@0: #    features_vector_estimated = zeros(shape(features_vector))
e@0: #    
e@0: #
e@0: #    for n in range(0, len(features_vector_estimated[0])):
e@0: #        features_vector_estimated[:,n] = features_clustering_means[features_clustering_labels[n]]
e@0: #        
e@0: #  #  for n in range(0,features_vector.shape[0]):
e@0: #   # hist(features_vector[1]-features_vector_estimated[1], 256)
e@0: #    std(features_vector[1]-features_vector_estimated[1], ddof=1)
e@0: #    mean(features_vector[1]-features_vector_estimated[1])
e@0: #    
e@0: #    print "[II] Features MSE for %d clusters: %.3f" % (n_clusters, mse(features_vector, features_vector_estimated))
e@0: #        
e@0: #        
e@0: #    print "[II] Testing Gaussian Naive Bayes Classifier"
e@0: #
e@0: #    gnb = GaussianNB()    
e@0: #    gnb.fit(features_vector.T, parameters_k_means_labels)
e@0: #    parameter_labels_estimated = gnb.predict(features_vector.T)
e@0: #    
e@0: #    print "[II] Raw Parameters - Gaussian Naive Bayes classifier testing ratio: %.4f" % (float(sum((parameter_labels_estimated == parameters_k_means_labels).astype(float)))/float(len(parameters_k_means_labels)))
e@0: #    
e@0: #    
e@0: #    print "[II] Trying ADM-coded parameters"
e@0: #    UpsamplingFactor = 100
e@0: #    print "[II] Upsampling features and parameters by a factor of %d" % UpsamplingFactor
e@0: #    
e@0: #
e@0: #    # Upsampled features and parameters    
e@0: #    features_vector_upsampled = repeat(features_vector,UpsamplingFactor, axis=1)
e@0: #    feature_labels_upsampled = repeat(features_clustering_labels,UpsamplingFactor, axis=0)
e@0: #    parameters_vector_upsampled = repeat(parameters_vector,UpsamplingFactor, axis=1)
e@0: #    
e@0: #    parameters_vector_upsampled_adm = matrix(zeros(shape(parameters_vector_upsampled)))
e@0: #    parameters_vector_upsampled_code = matrix(zeros(shape(parameters_vector_upsampled)))
e@0: #    parameters_vector_upsampled_firstval = matrix(zeros((parameters_vector_upsampled.shape[0],1)))
e@0: #    
e@0: #    # Reconstructed parameters
e@0: #    
e@0: #    parameters_vector_upsampled_reconstructed = matrix(zeros(shape(parameters_vector_upsampled)))
e@0: #    
e@0: #    dmin = 0.001
e@0: #    dmax = 0.28
e@0: #    tol = 0.001    
e@0: #    for i in range(0, parameters_vector_upsampled.shape[0]):
e@0: #        out, code, h =  adm(parameters_vector_upsampled[i,:],dmin=dmin,dmax=dmax,tol=tol)
e@0: #        parameters_vector_upsampled_adm[i,:] = out
e@0: #        parameters_vector_upsampled_code[i,:] = code
e@0: #        parameters_vector_upsampled_firstval[i, 0] = h
e@0: #        
e@0: #        
e@0: #        # Reconstruct-ADM        
e@0: #        parameters_vector_upsampled_reconstructed[i,:] = adm_reconstruct(parameters_vector_upsampled_code[i,:],parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
e@0: #        
e@0: #    
e@0: # #   plot(parameters_vector_upsampled_adm.T, 'r--')
e@0: #    
e@0: #    
e@0: #   # plot(parameters_vector_upsampled.T)
e@0: #  #  plot(parameters_vector_upsampled_reconstructed.T, 'g.')
e@0: #    
e@0: #    
e@0: #    
e@0: #    parameters_vector_reconstructed = zeros(shape(parameters_vector))
e@0: #    for n in range(0, parameters_vector.shape[1]):
e@0: #        parameters_vector_reconstructed[:,n] = parameters_vector_upsampled_reconstructed[:,n*UpsamplingFactor]
e@0: #        
e@0: #        
e@0: #    mse_adm = mse(parameters_vector_reconstructed, parameters_vector)
e@0: #    
e@0: #    print "[II] Expected ADM reconstruction MSE: %.4f" % mse_adm
e@0: #    
e@0: #   # figure()
e@0: #    #plot(parameters_vector.T)
e@0: #   # plot(parameters_vector_reconstructed.T)
e@0: #    
e@0: #    print "[II] Building HMM transition, emission matrices and priors"
e@0: #    
e@0: #    transmat = zeros((3,3))
e@0: #    startprob = zeros((3,))
e@0: #    emissionmat = zeros((3, n_clusters))
e@0: #    
e@0: #            
e@0: #    state_labels = parameters_vector_upsampled_code + 1
e@0: #    stateseq = state_labels.T
e@0: #    
e@0: #    for n in range(0,shape(parameters_vector_upsampled_code)[1]):
e@0: #        if n>0:
e@0: #            transmat[state_labels[0,n-1],state_labels[0,n]] += 1
e@0: #        startprob[state_labels[0,n]] +=1
e@0: #        emissionmat[state_labels[0,n],feature_labels_upsampled[n]] += 1
e@0: #        
e@0: #        
e@0: #    for n in range(0, transmat.shape[0]):
e@0: #        transmat[n,:]/=sum(transmat[n,:])
e@0: #        emissionmat[n,:]/=sum(emissionmat[n,:])
e@0: #         
e@0: #
e@0: #    transmat = matrix(transmat)
e@0: #    emissionmat = matrix(emissionmat)
e@0: #    # Prior
e@0: #    startprob = startprob/sum(startprob)
e@0: #    startprob = ravel(startprob)
e@0: #    
e@0: #    # Vocabulary
e@0: #        
e@0: #    model = hmm.GMMHMM(n_mix=n_clusters, n_components=3, covariance_type="diag")
e@0: #    model.means_ = features_clustering.means_
e@0: #    model.covars_ = features_clustering.covars_   
e@0: #    
e@0: #    features_vector_array = array(features_vector)
e@0: #    features_vector_upsampled_array=array(features_vector_upsampled)
e@0: #    
e@0: #    model.fit([features_vector_array.T])
e@0: #    stateseq_estimated = model.predict(features_vector_upsampled_array.T)
e@0: #    
e@0: #    parameters_vector_upsampled_reconstructed_decoded = matrix(zeros(shape(parameters_vector_upsampled)))
e@0: # 
e@0: #
e@0: #    plot(stateseq_estimated)
e@0: #    plot(stateseq)
e@0: #    
e@0: #    code_estimated = matrix(zeros(shape(parameters_vector_upsampled)))
e@0: #    code_estimated[0,:] = stateseq_estimated - 1
e@0: #    
e@0: #    
e@0: #   
e@0: #    parameters_vector_upsampled_reconstructed_estimated = matrix(zeros(shape(parameters_vector_upsampled)))
e@0: #
e@0: #    for i in range(0, parameters_vector_upsampled.shape[0]):
e@0: #        parameters_vector_upsampled_reconstructed_estimated[i,:] = adm_reconstruct(code_estimated,parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
e@0: #    figure()
e@0: #    plot(parameters_vector_upsampled_reconstructed_estimated.T)
e@0: #    plot(parameters_vector_upsampled.T)