diff experiment-reverb/code/supervised_training_pca-bak.py @ 0:246d5546657c

initial commit, needs cleanup

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/experiment-reverb/code/supervised_training_pca-bak.py	Wed Dec 14 13:15:48 2016 +0000
@@ -0,0 +1,440 @@
+#!/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 hmmlearn import hmm
+from sklearn 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)
+        
+    
+    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_
+
+        
+    
+    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_lept_data = features_vector
+    
+    # Generate the parameter clusters using k-means
+    
+    # Uncomment this
+    features_vector = (kernel.T*features_kept_data)[0:q,:]
+    
+    
+   
+ #   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)
+    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."
+#    
+#    n_clusters = 100
+#    features_clustering = GMM(n_components = n_clusters, covariance_type='diag')
+#    
+#    features_clustering.fit( features_vector.T, y=parameters_k_means_labels)
+#    
+#    features_clustering_means = features_clustering.means_
+#    features_clustering_labels = features_clustering.predict(features_vector.T)
+#    features_clustering_sigmas = features_clustering.covars_
+#    
+#    features_vector_estimated = zeros(shape(features_vector))
+#    
+#
+#    for n in range(0, len(features_vector_estimated[0])):
+#        features_vector_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, features_vector_estimated))
+#        
+#        
+#    print "[II] Testing Gaussian Naive Bayes Classifier"
+#
+#    gnb = GaussianNB()    
+#    gnb.fit(features_vector.T, parameters_k_means_labels)
+#    parameter_labels_estimated = gnb.predict(features_vector.T)
+#    
+#    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)))
+#    
+#    
+#    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)
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