From 3273ccd2874854bb36feed2ab0b2bf0989939cc2 Mon Sep 17 00:00:00 2001
From: Manuel Gunther <siebenkopf@googlemail.com>
Date: Mon, 11 Apr 2016 13:41:11 -0600
Subject: [PATCH] Removed some unused files
---
bob/learn/em/.DS_Store | Bin 6148 -> 0 bytes
bob/learn/em/cpp/BICMachine.cpp | 348 --------------------------------
bob/learn/em/cpp/BICTrainer.cpp | 94 ---------
bob/learn/em/include/.DS_Store | Bin 6148 -> 0 bytes
4 files changed, 442 deletions(-)
delete mode 100644 bob/learn/em/.DS_Store
delete mode 100644 bob/learn/em/cpp/BICMachine.cpp
delete mode 100644 bob/learn/em/cpp/BICTrainer.cpp
delete mode 100644 bob/learn/em/include/.DS_Store
diff --git a/bob/learn/em/.DS_Store b/bob/learn/em/.DS_Store
deleted file mode 100644
index 47a80a664e3fd38c9114f0953bbebcd70e7d0146..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/bob/learn/em/cpp/BICMachine.cpp b/bob/learn/em/cpp/BICMachine.cpp
deleted file mode 100644
index 4abe724..0000000
--- a/bob/learn/em/cpp/BICMachine.cpp
+++ /dev/null
@@ -1,348 +0,0 @@
-/**
- * @date Tue Jun 5 16:54:27 CEST 2012
- * @author Manuel Guenther <Manuel.Guenther@idiap.ch>
- *
- * A machine that implements the liner projection of input to the output using
- * weights, biases and sums:
- * output = sum(inputs * weights) + bias
- * It is possible to setup the machine to previously normalize the input taking
- * into consideration some input bias and division factor. It is also possible
- * to set it up to have an activation function.
- * A linear classifier. See C. M. Bishop, "Pattern Recognition and Machine
- * Learning", chapter 4
- *
- * Copyright (C) Idiap Research Institute, Martigny, Switzerland
- */
-
-#include <bob.learn.em/BICMachine.h>
-#include <bob.math/linear.h>
-#include <bob.core/assert.h>
-#include <bob.core/check.h>
-
-/**
- * Initializes an empty BIC Machine
- *
- * @param use_DFFS Add the Distance From Feature Space during score computation?
- */
-bob::learn::em::BICMachine::BICMachine(bool use_DFFS)
-:
- m_project_data(use_DFFS),
- m_use_DFFS(use_DFFS)
-{}
-
-/**
- * Assigns the other BICMachine to this, i.e., makes a deep copy of the given machine.
- *
- * @param other The other BICMachine to get a shallow copy of
- * @return a reference to *this
- */
-bob::learn::em::BICMachine::BICMachine(const BICMachine& other)
-:
- m_project_data(other.m_project_data),
- m_use_DFFS(other.m_use_DFFS)
-{
- if (m_project_data){
- setBIC(false, other.m_mu_I, other.m_lambda_I, other.m_Phi_I, other.m_rho_I, true);
- setBIC(true , other.m_mu_E, other.m_lambda_E, other.m_Phi_E, other.m_rho_E, true);
- } else {
- setIEC(false, other.m_mu_I, other.m_lambda_I, true);
- setIEC(true , other.m_mu_E, other.m_lambda_E, true);
- }
-}
-
-/**
- * Assigns the other BICMachine to this, i.e., makes a deep copy of the given BICMachine
- *
- * @param other The other BICMachine to get a deep copy of
- * @return a reference to *this
- */
-bob::learn::em::BICMachine& bob::learn::em::BICMachine::operator=(const BICMachine& other)
-{
- if (this != &other)
- {
- if (other.m_project_data){
- m_use_DFFS = other.m_use_DFFS;
- setBIC(false, other.m_mu_I, other.m_lambda_I, other.m_Phi_I, other.m_rho_I, true);
- setBIC(true , other.m_mu_E, other.m_lambda_E, other.m_Phi_E, other.m_rho_E, true);
- } else {
- m_use_DFFS = false;
- setIEC(false, other.m_mu_I, other.m_lambda_I, true);
- setIEC(true , other.m_mu_E, other.m_lambda_E, true);
- }
- }
- return *this;
-}
-
-/**
- * Compares if this machine and the given one are identical
- *
- * @param other The BICMachine to compare with
- * @return true if both machines are identical, i.e., have exactly the same parameters, otherwise false
- */
-bool bob::learn::em::BICMachine::operator==(const BICMachine& other) const
-{
- return (m_project_data == other.m_project_data &&
- (!m_project_data || m_use_DFFS == other.m_use_DFFS) &&
- bob::core::array::isEqual(m_mu_I, other.m_mu_I) &&
- bob::core::array::isEqual(m_mu_E, other.m_mu_E) &&
- bob::core::array::isEqual(m_lambda_I, other.m_lambda_I) &&
- bob::core::array::isEqual(m_lambda_E, other.m_lambda_E) &&
- (!m_project_data ||
- (bob::core::array::isEqual(m_Phi_I, other.m_Phi_I) &&
- bob::core::array::isEqual(m_Phi_E, other.m_Phi_E) &&
- (!m_use_DFFS || (m_rho_I == other.m_rho_I && m_rho_E == other.m_rho_E)))));
-}
-
-/**
- * Checks if this machine and the given one are different
- *
- * @param other The BICMachine to compare with
- * @return false if both machines are identical, i.e., have exactly the same parameters, otherwise true
- */
-bool bob::learn::em::BICMachine::operator!=(const BICMachine& other) const
-{
- return !(this->operator==(other));
-}
-
-/**
- * Compares the given machine with this for similarity
- *
- * @param other The BICMachine to compare with
- * @param r_epsilon The largest value any parameter might relatively differ between the two machines
- * @param a_epsilon The largest value any parameter might absolutely differ between the two machines
-
- * @return true if both machines are approximately equal, otherwise false
- */
-bool bob::learn::em::BICMachine::is_similar_to(const BICMachine& other,
- const double r_epsilon, const double a_epsilon) const
-{
- if (m_project_data){
- // compare data
- if (not bob::core::array::hasSameShape(m_Phi_I, other.m_Phi_I)) return false;
- if (not bob::core::array::hasSameShape(m_Phi_E, other.m_Phi_E)) return false;
- // check that the projection matrices are close,
- // but allow that eigen vectors might have opposite directions
- // (i.e., they are either identical -> difference is 0, or opposite -> sum is zero)
- for (int i = m_Phi_I.extent(1); i--;){
- const blitz::Array<double,1>& sub1 = m_Phi_I(blitz::Range::all(), i);
- const blitz::Array<double,1>& sub2 = other.m_Phi_I(blitz::Range::all(), i);
- blitz::Array<double,1> sub2_negative(-sub2);
- if (!bob::core::array::isClose(sub1, sub2, r_epsilon, a_epsilon) && !bob::core::array::isClose(sub1, sub2_negative, r_epsilon, a_epsilon)) return false;
- }
- for (int i = m_Phi_E.shape()[1]; i--;){
- const blitz::Array<double,1>& sub1 = m_Phi_E(blitz::Range::all(), i);
- const blitz::Array<double,1>& sub2 = other.m_Phi_E(blitz::Range::all(), i);
- blitz::Array<double,1> sub2_negative(-sub2);
- if (!bob::core::array::isClose(sub1, sub2, r_epsilon, a_epsilon) && !bob::core::array::isClose(sub1, sub2_negative, r_epsilon, a_epsilon)) return false;
- }
- }
-
- return (m_project_data == other.m_project_data &&
- (!m_project_data || m_use_DFFS == other.m_use_DFFS) &&
- bob::core::array::isClose(m_mu_I, other.m_mu_I, r_epsilon, a_epsilon) &&
- bob::core::array::isClose(m_mu_E, other.m_mu_E, r_epsilon, a_epsilon) &&
- bob::core::array::isClose(m_lambda_I, other.m_lambda_I, r_epsilon, a_epsilon) &&
- bob::core::array::isClose(m_lambda_E, other.m_lambda_E, r_epsilon, a_epsilon) &&
- (!m_project_data ||
- (!m_use_DFFS || (bob::core::isClose(m_rho_I, other.m_rho_I, r_epsilon, a_epsilon) &&
- bob::core::isClose(m_rho_E, other.m_rho_E, r_epsilon, a_epsilon)))));
-}
-
-
-
-void bob::learn::em::BICMachine::initialize(bool clazz, int input_length, int projected_length){
- blitz::Array<double,1>& diff = clazz ? m_diff_E : m_diff_I;
- blitz::Array<double,1>& proj = clazz ? m_proj_E : m_proj_I;
- diff.resize(input_length);
- proj.resize(projected_length);
-}
-
-/**
- * Sets the parameters of the given class that are required for computing the IEC scores (Guenther, Wuertz)
- *
- * @param clazz false for the intrapersonal class, true for the extrapersonal one.
- * @param mean The mean vector of the training data
- * @param variances The variances of the training data
- * @param copy_data If true, makes a deep copy of the matrices, otherwise it just references it (the default)
- */
-void bob::learn::em::BICMachine::setIEC(
- bool clazz,
- const blitz::Array<double,1>& mean,
- const blitz::Array<double,1>& variances,
- bool copy_data
-){
- m_project_data = false;
- // select the right matrices to write
- blitz::Array<double,1>& mu = clazz ? m_mu_E : m_mu_I;
- blitz::Array<double,1>& lambda = clazz ? m_lambda_E : m_lambda_I;
-
- // copy mean and variances
- if (copy_data){
- mu.resize(mean.shape());
- mu = mean;
- lambda.resize(variances.shape());
- lambda = variances;
- } else {
- mu.reference(mean);
- lambda.reference(variances);
- }
-}
-
-/**
- * Sets the parameters of the given class that are required for computing the BIC scores (Teixeira)
- *
- * @param clazz false for the intrapersonal class, true for the extrapersonal one.
- * @param mean The mean vector of the training data
- * @param variances The eigenvalues of the training data
- * @param projection The PCA projection matrix
- * @param rho The residual eigenvalues, used for DFFS calculation
- * @param copy_data If true, makes a deep copy of the matrices, otherwise it just references it (the default)
- */
-void bob::learn::em::BICMachine::setBIC(
- bool clazz,
- const blitz::Array<double,1>& mean,
- const blitz::Array<double,1>& variances,
- const blitz::Array<double,2>& projection,
- const double rho,
- bool copy_data
-){
- m_project_data = true;
- // select the right matrices to write
- blitz::Array<double,1>& mu = clazz ? m_mu_E : m_mu_I;
- blitz::Array<double,1>& lambda = clazz ? m_lambda_E : m_lambda_I;
- blitz::Array<double,2>& Phi = clazz ? m_Phi_E : m_Phi_I;
- double& rho_ = clazz ? m_rho_E : m_rho_I;
-
- // copy information
- if (copy_data){
- mu.resize(mean.shape());
- mu = mean;
- lambda.resize(variances.shape());
- lambda = variances;
- Phi.resize(projection.shape());
- Phi = projection;
- } else {
- mu.reference(mean);
- lambda.reference(variances);
- Phi.reference(projection);
- }
- rho_ = rho;
-
- // check that rho has a reasonable value (if it is used)
- if (m_use_DFFS && rho_ < 1e-12) throw std::runtime_error("The given average eigenvalue (rho) is too close to zero");
-
- // initialize temporaries
- initialize(clazz, Phi.shape()[0], Phi.shape()[1]);
-}
-
-/**
- * Set or unset the usage of the Distance From Feature Space
- *
- * @param use_DFFS The new value of use_DFFS
- */
-void bob::learn::em::BICMachine::use_DFFS(bool use_DFFS){
- m_use_DFFS = use_DFFS;
- if (m_project_data && m_use_DFFS && (m_rho_E < 1e-12 || m_rho_I < 1e-12)) std::runtime_error("The average eigenvalue (rho) is too close to zero, so using DFFS will not work");
-}
-
-/**
- * Loads the BICMachine from the given hdf5 file.
- *
- * @param config The hdf5 file containing the required information.
- */
-void bob::learn::em::BICMachine::load(bob::io::base::HDF5File& config){
- //reads all data directly into the member variables
- m_project_data = config.read<bool>("project_data");
- m_mu_I.reference(config.readArray<double,1>("intra_mean"));
- m_lambda_I.reference(config.readArray<double,1>("intra_variance"));
- if (m_project_data){
- m_use_DFFS = config.read<bool>("use_DFFS");
- m_Phi_I.reference(config.readArray<double,2>("intra_subspace"));
- initialize(false, m_Phi_I.shape()[0], m_Phi_I.shape()[1]);
- m_rho_I = config.read<double>("intra_rho");
- }
-
- m_mu_E.reference(config.readArray<double,1>("extra_mean"));
- m_lambda_E.reference(config.readArray<double,1>("extra_variance"));
- if (m_project_data){
- m_Phi_E.reference(config.readArray<double,2>("extra_subspace"));
- initialize(true, m_Phi_E.shape()[0], m_Phi_E.shape()[1]);
- m_rho_E = config.read<double>("extra_rho");
- }
- // check that rho has reasonable values
- if (m_project_data && m_use_DFFS && (m_rho_E < 1e-12 || m_rho_I < 1e-12)) throw std::runtime_error("The loaded average eigenvalue (rho) is too close to zero");
-
-}
-
-/**
- * Saves the parameters of the BICMachine to the given hdf5 file.
- *
- * @param config The hdf5 file to write the configuration into.
- */
-void bob::learn::em::BICMachine::save(bob::io::base::HDF5File& config) const{
- config.set("project_data", m_project_data);
- config.setArray("intra_mean", m_mu_I);
- config.setArray("intra_variance", m_lambda_I);
- if (m_project_data){
- config.set("use_DFFS", m_use_DFFS);
- config.setArray("intra_subspace", m_Phi_I);
- config.set("intra_rho", m_rho_I);
- }
-
- config.setArray("extra_mean", m_mu_E);
- config.setArray("extra_variance", m_lambda_E);
- if (m_project_data){
- config.setArray("extra_subspace", m_Phi_E);
- config.set("extra_rho", m_rho_E);
- }
-}
-
-/**
- * Computes the BIC or IEC score for the given input vector.
- * The score itself is the log-likelihood score of the given input vector belonging to the intrapersonal class.
- * No sanity checks of input and output are performed.
- *
- * @param input A vector (of difference values) to compute the BIC or IEC score for.
- * @param output The one-element array that will contain the score afterwards.
- */
-void bob::learn::em::BICMachine::forward_(const blitz::Array<double,1>& input, double& output) const{
- if (m_project_data){
- // subtract mean
- m_diff_I = input - m_mu_I;
- m_diff_E = input - m_mu_E;
- // project data to intrapersonal and extrapersonal subspace
- bob::math::prod(m_diff_I, m_Phi_I, m_proj_I);
- bob::math::prod(m_diff_E, m_Phi_E, m_proj_E);
-
- // compute Mahalanobis distance
- output = blitz::sum(blitz::pow2(m_proj_E) / m_lambda_E) - blitz::sum(blitz::pow2(m_proj_I) / m_lambda_I);
-
- // add the DFFS?
- if (m_use_DFFS){
- output += blitz::sum(blitz::pow2(m_diff_E) - blitz::pow2(m_proj_E)) / m_rho_E;
- output -= blitz::sum(blitz::pow2(m_diff_I) - blitz::pow2(m_proj_I)) / m_rho_I;
- }
- output /= (m_proj_E.extent(0) + m_proj_I.extent(0));
- } else {
- // forward without projection
- output = blitz::mean( blitz::pow2(input - m_mu_E) / m_lambda_E
- - blitz::pow2(input - m_mu_I) / m_lambda_I);
- }
-}
-
-/**
- * Computes the BIC or IEC score for the given input vector.
- * The score itself is the log-likelihood score of the given input vector belonging to the intrapersonal class.
- * Sanity checks of input and output shape are performed.
- *
- * @param input A vector (of difference values) to compute the BIC or IEC score for.
- * @param output The one-element array that will contain the score afterwards.
- */
-void bob::learn::em::BICMachine::forward(const blitz::Array<double,1>& input, double& output) const{
- // perform some checks
- bob::core::array::assertSameShape(input, m_mu_E);
-
- // call the actual method
- forward_(input, output);
-}
-
diff --git a/bob/learn/em/cpp/BICTrainer.cpp b/bob/learn/em/cpp/BICTrainer.cpp
deleted file mode 100644
index 16d0726..0000000
--- a/bob/learn/em/cpp/BICTrainer.cpp
+++ /dev/null
@@ -1,94 +0,0 @@
-/**
- * @date Wed Jun 6 10:29:09 CEST 2012
- * @author Manuel Guenther <Manuel.Guenther@idiap.ch>
- *
- * Copyright (C) Idiap Research Institute, Martigny, Switzerland
- */
-
-#include <bob.learn.em/BICTrainer.h>
-#include <bob.learn.linear/pca.h>
-#include <bob.learn.linear/machine.h>
-
-static double sqr(const double& x){
- return x*x;
-}
-
-/**
- * This function trains one of the classes of the given machine with the given data.
- * It computes either BIC projection matrices, or IEC mean and variance.
- *
- * @param clazz false for the intrapersonal class, true for the extrapersonal one.
- * @param machine The machine to be trained.
- * @param differences A set of (intra/extra)-personal difference vectors that should be trained.
- */
-void bob::learn::em::BICTrainer::train_single(bool clazz, bob::learn::em::BICMachine& machine, const blitz::Array<double,2>& differences) const {
- int subspace_dim = clazz ? m_M_E : m_M_I;
- int input_dim = differences.extent(1);
- int data_count = differences.extent(0);
- blitz::Range a = blitz::Range::all();
-
- if (subspace_dim){
- // train the class using BIC
-
- // Compute PCA on the given dataset
- bob::learn::linear::PCATrainer trainer;
- const int n_eigs = trainer.output_size(differences);
- bob::learn::linear::Machine pca(input_dim, n_eigs);
- blitz::Array<double,1> variances(n_eigs);
- trainer.train(pca, variances, differences);
-
- // compute rho
- double rho = 0.;
- int non_zero_eigenvalues = std::min(input_dim, data_count-1);
- // assert that the number of kept eigenvalues is not chosen to big
- if (subspace_dim >= non_zero_eigenvalues)
- throw std::runtime_error((boost::format("The chosen subspace dimension %d is larger than the theoretical number of nonzero eigenvalues %d")%subspace_dim%non_zero_eigenvalues).str());
- // compute the average of the reminding eigenvalues
- for (int i = subspace_dim; i < non_zero_eigenvalues; ++i){
- rho += variances(i);
- }
- rho /= non_zero_eigenvalues - subspace_dim;
-
- // limit dimensionalities
- pca.resize(input_dim, subspace_dim);
- variances.resizeAndPreserve(subspace_dim);
-
- // check that all variances are meaningful
- for (int i = 0; i < subspace_dim; ++i){
- if (variances(i) < 1e-12)
- throw std::runtime_error((boost::format("The chosen subspace dimension is %d, but the %dth eigenvalue is already to small")%subspace_dim%i).str());
- }
-
- // initialize the machine
- blitz::Array<double, 2> projection = pca.getWeights();
- blitz::Array<double, 1> mean = pca.getInputSubtraction();
- machine.setBIC(clazz, mean, variances, projection, rho);
- } else {
- // train the class using IEC
- // => compute mean and variance only
- blitz::Array<double,1> mean(input_dim), variance(input_dim);
-
- // compute mean and variance
- mean = 0.;
- variance = 0.;
- for (int n = data_count; n--;){
- const blitz::Array<double,1>& diff = differences(n,a);
- assert(diff.shape()[0] == input_dim);
- for (int i = input_dim; i--;){
- mean(i) += diff(i);
- variance(i) += sqr(diff(i));
- }
- }
- // normalize mean and variances
- for (int i = 0; i < input_dim; ++i){
- // intrapersonal
- variance(i) = (variance(i) - sqr(mean(i)) / data_count) / (data_count - 1.);
- mean(i) /= data_count;
- if (variance(i) < 1e-12)
- throw std::runtime_error((boost::format("The variance of the %dth dimension is too small. Check your data!")%i).str());
- }
-
- // set the results to the machine
- machine.setIEC(clazz, mean, variance);
- }
-}
diff --git a/bob/learn/em/include/.DS_Store b/bob/learn/em/include/.DS_Store
deleted file mode 100644
index 34b4a20f450a2fe6b32abb36f282ba6e1cc123f8..0000000000000000000000000000000000000000
GIT binary patch
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