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PMF.cpp

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/************************************************************************
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#include "problib/pdfs/PMF.h"

using namespace pbl;

PMF::PMF(int domain_size) : PDF(1, PDF::DISCRETE), ptr_(new PMFStruct(domain_size)) {
}

PMF::PMF(const PMF& pmf) : PDF(1, PDF::DISCRETE), ptr_(pmf.ptr_) {
    if (ptr_) {
        ++ptr_->n_ptrs_;
    }
}

PMF::~PMF() {
        --ptr_->n_ptrs_;

        if (ptr_->n_ptrs_ == 0) {
                delete ptr_;
        }
}

PMF& PMF::operator=(const PMF& other)  {
    if (this != &other)  {
        if (ptr_) {
                        --ptr_->n_ptrs_;
                        if (ptr_->n_ptrs_ == 0) {
                                delete ptr_;
                        }
        }

        ptr_ = other.ptr_;
        ++ptr_->n_ptrs_;

        dimensions_ = other.dimensions_;
    }
    return *this;
}

PMF* PMF::clone() const {
        return new PMF(*this);
}

void PMF::cloneStruct() {
        if (ptr_->n_ptrs_ > 1) {
                --ptr_->n_ptrs_;
                ptr_ = new PMFStruct(*ptr_);
        }
}

double PMF::getProbability(const std::string& value) const {
        return getProbability(value, ptr_->domain_size_);
}

double PMF::getProbability(const std::string& value, int domain_size) const {
        std::map<std::string, double>::const_iterator it = ptr_->pmf_.find(value);
        if (it != ptr_->pmf_.end()) {
                return (*it).second;
        }
        // if now probability is known for this value, calculate its probability
        // based on a uniform distribution over all unknown values.
        return getProbabilityUnknown(domain_size);
}

void PMF::setProbability(const std::string& value, double p) {
        cloneStruct();

        std::map<std::string, double>::iterator it = ptr_->pmf_.find(value);
        if (it != ptr_->pmf_.end()) {
                ptr_->total_prob_ -= (*it).second;
                (*it).second = p;
        } else {
                ptr_->pmf_[value] = p;
        }
        ptr_->total_prob_ += p;
}

void PMF::setExact(const std::string& value) {
        if (ptr_->n_ptrs_ > 1) {
                --ptr_->n_ptrs_;
                ptr_ = new PMFStruct(ptr_->domain_size_);
        } else {
                ptr_->pmf_.clear();
        }
        ptr_->pmf_[value] = 1.0;
        ptr_->total_prob_ = 1.0;
}

void PMF::getValues(std::vector<std::string>& values) const {
        for(std::map<std::string, double>::const_iterator it = ptr_->pmf_.begin(); it != ptr_->pmf_.end(); ++it) {
                values.push_back((*it).first);
        }
}

void PMF::getProbabilities(std::vector<double>& probabilities) const {
        for(std::map<std::string, double>::const_iterator it = ptr_->pmf_.begin(); it != ptr_->pmf_.end(); ++it) {
                probabilities.push_back((*it).second);
        }
}

bool PMF::getExpectedValue(std::string& v) const {
        double p_max = 0;
        for(std::map<std::string, double>::const_iterator it = ptr_->pmf_.begin(); it != ptr_->pmf_.end(); ++it) {
                if ((*it).second > p_max) {
                        v = (*it).first;
                        p_max = (*it).second;
                }
        }

        if (ptr_->domain_size_ > 0 && p_max < getProbabilityUnknown(ptr_->domain_size_)) {
        v = "";
                return false;
        }

        return true;
}

double PMF::getLikelihood(const PDF& pdf) const {
        assert_msg(pdf.type() == PDF::DISCRETE, "PMF: Likelihood can only be calculated with another PMF.");

        const PMF* pmf = static_cast<const pbl::PMF*>(&pdf);
        return getLikelihood(*pmf);
}

double PMF::getLikelihood(const PMF& other) const {
        int my_domain_size = ptr_->domain_size_;
        int other_domain_size = other.ptr_->domain_size_;

        assert(my_domain_size == -1 || other_domain_size == -1 || my_domain_size == other_domain_size);
        int domain_size = std::max(my_domain_size, other_domain_size);

        // determine which pmf has the most determined values, and which one less
        const PMF* small_pmf = this;
        const PMF* big_pmf = &other;
        if (this->ptr_->pmf_.size() > other.ptr_->pmf_.size()) {
                small_pmf = &other;
                big_pmf = this;
        }

        // determine the likelihood based on all determined values in small_pmf
        double likelihood = 0;
        double big_p_total_match = 0;  // keeps track of the total probability of all values in big_pmf
                                                                   // that are matched to small_pmf
        for(std::map<std::string, double>::const_iterator it = small_pmf->ptr_->pmf_.begin(); it != small_pmf->ptr_->pmf_.end(); ++it) {
                double big_p_v = big_pmf->getProbability((*it).first, domain_size);
                likelihood += big_p_v * (*it).second;
                big_p_total_match += big_p_v;
        }

        // if the determined values in small_pdf do NOT add up to probability 1, AND
        // we have not yet matched all values in big_pmf with a total probability of 1, it means
        // we still need to match the undetermined values in small_pmf and big_pmf. ASSUME a
        // UNIFORM DISTRIBUTION over the undetermined values in both small_pmf and big_pmf
        if (small_pmf->ptr_->total_prob_ < 1 && big_p_total_match < 1) {
                // determine the number of unmatched values. Since we used small_pmf as a basis for
                // matching above (we matched all its determined values), the number of unmatched values
                // equals the domain size MINUS the number of determined values
                int num_unmatched_values = (domain_size - small_pmf->ptr_->pmf_.size());
                assert(num_unmatched_values > 0);

                // determine the average probability of an unknown value in big_pmf, assuming
                // a uniform distribution over all unknown values.
                double big_p_unknown   = (1 - big_p_total_match)      / num_unmatched_values;
                // determine the probability of an unknown value in small_pmf, assuming
                // a uniform distribution over all unknown values
                double small_p_unknown = (1 - small_pmf->ptr_->total_prob_) / num_unmatched_values;

                // add to the likelihood the SUM of big_p_unknown * small_p_unknown over all
                // unmatches values, which equals num_unmatched_values * big_p_unknown * small_p_unknown
                likelihood += num_unmatched_values * big_p_unknown * small_p_unknown;
        }

        return likelihood;
}

void PMF::update(const pbl::PMF& other) {
    assert(this->ptr_->domain_size_ == -1 || other.ptr_->domain_size_ == -1
                || this->ptr_->domain_size_ == other.ptr_->domain_size_);

    cloneStruct();

    this->ptr_->domain_size_ = std::max(this->ptr_->domain_size_, other.ptr_->domain_size_);

        // calculate likelihood
        double likelihood = getLikelihood(other);

        // calculate the current probability of unknown values
        double p_unknown = getProbabilityUnknown(this->ptr_->domain_size_);

        std::set<std::string> updated_values;

        ptr_->total_prob_ = 0;
        for(std::map<std::string, double>::iterator it = ptr_->pmf_.begin(); it != ptr_->pmf_.end(); ++it) {
                double new_prob = (*it).second * other.getProbability((*it).first, this->ptr_->domain_size_) / likelihood;
                (*it).second = new_prob;
                ptr_->total_prob_ += new_prob;
                updated_values.insert((*it).first);
        }

        for(std::map<std::string, double>::const_iterator it = other.ptr_->pmf_.begin(); it != other.ptr_->pmf_.end(); ++it) {
                if (updated_values.find((*it).first) == updated_values.end()) {
                        double new_prob = p_unknown * (*it).second / likelihood;
                        ptr_->pmf_[(*it).first] = new_prob;
                        ptr_->total_prob_ += new_prob;
                }
        }
}

void PMF::setDomainSize(int domain_size) {
        cloneStruct();
        ptr_->domain_size_ = domain_size;
}

int PMF::getDomainSize() const {
        return ptr_->domain_size_;
}

double PMF::getProbabilityUnknown() const {
        return getProbabilityUnknown(ptr_->domain_size_);
}

double PMF::getProbabilityUnknown(int domain_size) const {
        if (ptr_->total_prob_ == 1) return 0;
        assert(domain_size > 0);
        if (domain_size == (int)ptr_->pmf_.size()) return 0;
        return (1 - ptr_->total_prob_) / (domain_size - ptr_->pmf_.size());
}

void PMF::normalize() {
        if (ptr_->total_prob_ == 1) return;

        assert(ptr_->total_prob_ > 0);

        cloneStruct();

        for(std::map<std::string, double>::iterator it = ptr_->pmf_.begin(); it != ptr_->pmf_.end(); ++it) {
                (*it).second /= ptr_->total_prob_;
        }

        ptr_->domain_size_ = ptr_->pmf_.size();

        ptr_->total_prob_ = 1;
}

double PMF::getDensity(const arma::vec& v) const {
        assert_msg(false, "Cannot get density of a PMF");
        return 0;
}

double PMF::getMaxDensity() const {
        assert_msg(false, "Cannot get max density of a PMF");
        return 0;
}

std::string PMF::toString(const std::string& indent) const {
        std::stringstream ss;
        ss << indent << "PMF(" << ptr_->domain_size_ << ")[";

        std::map<std::string, double>::const_iterator it = ptr_->pmf_.begin();
        if (it != ptr_->pmf_.end()) {
                ss << " " << (*it).first << " : " << (*it).second;
                ++it;
                for(; it != ptr_->pmf_.end(); ++it) {
                        ss << ", " << (*it).first << " : " << (*it).second;
                }
        }
        ss << " ]";
        return ss.str();
}

/* * * * * * * * OBSOLETE * * * * * * * * */

std::string PMF::getMostProbableValue() const {
        std::string v;
        getExpectedValue(v);
        return v;
}

---

problib
Author(s): Sjoerd van den Dries
autogenerated on Tue Mar 5 12:28:28 2013