コード例 #1
0
ファイル: nonlinearities.cpp プロジェクト: cajal/cmt
double CMT::ExponentialFunction::inverse(double data) const {
	return log(data - mEpsilon);
}
コード例 #2
0
ファイル: colorize.cpp プロジェクト: nikky4D/xform_recipes
void colorize(const Image<uint32_t> &input, const Image<uint32_t> &strokes, Image<uint32_t> &output) {
    int w     = input.width();
    int h     = input.height();
    int x,y;

    int max_d          = floor(log(min(h,w))/log(2)-2);
    float scale_factor = 1.0f/( pow(2,max_d-1) );
    int padded_w       = ceil(w*scale_factor)*pow(2,max_d-1);
    int padded_h       = ceil(h*scale_factor)*pow(2,max_d-1);

    // RGB 2 YUV and padarray
    Image<float> yuv_fused(padded_w,padded_h,3);
    hl_fuse_yuv(input, strokes, yuv_fused);


    // Extract Strokes mask
    Image<float> stroke_mask(padded_w, padded_h);
    hl_nonzero(strokes,stroke_mask);

    Image<float> result(padded_w,padded_h,3);

    int n = padded_h;
    int m = padded_w;
    int k = 1;

    Tensor3d D,G,I;
    Tensor3d Dx,Dy,iDx,iDy;
    MG smk;
    G.set(n,m,k);
    D.set(n,m,k);
    I.set(n,m,k);

    int in_itr_num  = 5;
    int out_itr_num = 1;

    Dx.set(n,m,k-1);
    Dy.set(n,m,k-1);
    iDx.set(n,m,k-1);
    iDy.set(n,m,k-1);

    // Fill in label mask and luminance channels
    for ( y = 0; y<n; y++){
        for ( x = 0; x<m; x++){
            I(y,x,0) = stroke_mask(x,y);
            G(y,x,0) = yuv_fused(x,y,0);
            I(y,x,0) = !I(y,x,0);
        }
    }

    // Write output luminance
    for ( y=0; y<n; y++){
        for ( x=0; x<m; x++){
            result(x,y,0)=G(y,x,0);
        }
    }

    smk.set(n,m,k,max_d);
    smk.setI(I) ;
    smk.setG(G);
    smk.setFlow(Dx,Dy,iDx,iDy);

    // Solve chrominance
    for (int t=1; t<3; t++){
        for ( y=0; y<n; y++){
            for ( x=0; x<m; x++){
                D(y,x,0)       = yuv_fused(x,y,t);
                smk.P()(y,x,0) = yuv_fused(x,y,t);
                D(y,x,0)      *= (!I(y,x,0));
            }
        }

        smk.Div() = D ;

        Tensor3d tP2;

        for (int itr=0; itr<out_itr_num; itr++){
            smk.setDepth(max_d);
            Field_MGN(&smk, in_itr_num, 2) ;
            smk.setDepth(ceil(max_d/2));
            Field_MGN(&smk, in_itr_num, 2) ;
            smk.setDepth(2);
            Field_MGN(&smk, in_itr_num, 2) ;
            smk.setDepth(1);
            Field_MGN(&smk, in_itr_num, 4) ;
        }

        tP2 = smk.P();

        for ( y=0; y<n; y++){
            for ( x=0; x<m; x++){
                result(x,y,t) = tP2(y,x,0);
            }
        }
    }
    
    hl_yuv2rgb(result,output);
    

}
コード例 #3
0
ファイル: normal_ccdf_log.hpp プロジェクト: aseyboldt/math
    typename return_type<T_y, T_loc, T_scale>::type
    normal_ccdf_log(const T_y& y, const T_loc& mu, const T_scale& sigma) {
      static const char* function("stan::math::normal_ccdf_log");
      typedef typename stan::partials_return_type<T_y, T_loc, T_scale>::type
        T_partials_return;

      using stan::math::check_positive;
      using stan::math::check_finite;
      using stan::math::check_not_nan;
      using stan::math::check_consistent_sizes;
      using stan::math::value_of;
      using stan::math::INV_SQRT_2;
      using std::log;
      using std::exp;

      T_partials_return ccdf_log(0.0);
      // check if any vectors are zero length
      if (!(stan::length(y)
            && stan::length(mu)
            && stan::length(sigma)))
        return ccdf_log;

      check_not_nan(function, "Random variable", y);
      check_finite(function, "Location parameter", mu);
      check_not_nan(function, "Scale parameter", sigma);
      check_positive(function, "Scale parameter", sigma);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Location parameter", mu,
                             "Scale parameter", sigma);

      OperandsAndPartials<T_y, T_loc, T_scale>
        operands_and_partials(y, mu, sigma);

      VectorView<const T_y> y_vec(y);
      VectorView<const T_loc> mu_vec(mu);
      VectorView<const T_scale> sigma_vec(sigma);
      size_t N = max_size(y, mu, sigma);
      double log_half = std::log(0.5);

      const double SQRT_TWO_OVER_PI = std::sqrt(2.0 / stan::math::pi());
      for (size_t n = 0; n < N; n++) {
        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return mu_dbl = value_of(mu_vec[n]);
        const T_partials_return sigma_dbl = value_of(sigma_vec[n]);

        const T_partials_return scaled_diff = (y_dbl - mu_dbl)
          / (sigma_dbl * SQRT_2);

        T_partials_return one_m_erf;
        if (scaled_diff < -37.5 * INV_SQRT_2)
          one_m_erf = 2.0;
        else if (scaled_diff < -5.0 * INV_SQRT_2)
          one_m_erf =  2.0 - erfc(-scaled_diff);
        else if (scaled_diff > 8.25 * INV_SQRT_2)
          one_m_erf = 0.0;
        else
          one_m_erf = 1.0 - erf(scaled_diff);

        // log ccdf
        ccdf_log += log_half + log(one_m_erf);

        // gradients
        if (contains_nonconstant_struct<T_y, T_loc, T_scale>::value) {
          const T_partials_return rep_deriv_div_sigma
            = scaled_diff > 8.25 * INV_SQRT_2
            ? std::numeric_limits<double>::infinity()
            : SQRT_TWO_OVER_PI * exp(-scaled_diff * scaled_diff)
            / one_m_erf / sigma_dbl;
          if (!is_constant_struct<T_y>::value)
            operands_and_partials.d_x1[n] -= rep_deriv_div_sigma;
          if (!is_constant_struct<T_loc>::value)
            operands_and_partials.d_x2[n] += rep_deriv_div_sigma;
          if (!is_constant_struct<T_scale>::value)
            operands_and_partials.d_x3[n] += rep_deriv_div_sigma
              * scaled_diff * stan::math::SQRT_2;
        }
      }
      return operands_and_partials.value(ccdf_log);
    }
コード例 #4
0
ファイル: transform_test.cpp プロジェクト: HerraHuu/stan
TEST(prob_transform, lb_f) {
  EXPECT_FLOAT_EQ(log(3.0 - 2.0), stan::prob::lb_free(3.0,2.0));
  EXPECT_FLOAT_EQ(1.7, stan::prob::lb_free(1.7, -std::numeric_limits<double>::infinity()));
}
コード例 #5
0
ファイル: lognormal_log.hpp プロジェクト: alyst/math
    typename return_type<T_y, T_loc, T_scale>::type
    lognormal_log(const T_y& y, const T_loc& mu, const T_scale& sigma) {
      static const char* function("stan::math::lognormal_log");
      typedef typename stan::partials_return_type<T_y, T_loc, T_scale>::type
        T_partials_return;

      using stan::is_constant_struct;
      using stan::math::check_not_nan;
      using stan::math::check_finite;
      using stan::math::check_positive_finite;
      using stan::math::check_nonnegative;
      using stan::math::check_consistent_sizes;
      using stan::math::value_of;
      using stan::math::include_summand;


      // check if any vectors are zero length
      if (!(stan::length(y)
            && stan::length(mu)
            && stan::length(sigma)))
        return 0.0;

      // set up return value accumulator
      T_partials_return logp(0.0);

      // validate args (here done over var, which should be OK)
      check_not_nan(function, "Random variable", y);
      check_nonnegative(function, "Random variable", y);
      check_finite(function, "Location parameter", mu);
      check_positive_finite(function, "Scale parameter", sigma);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Location parameter", mu,
                             "Scale parameter", sigma);

      VectorView<const T_y> y_vec(y);
      VectorView<const T_loc> mu_vec(mu);
      VectorView<const T_scale> sigma_vec(sigma);
      size_t N = max_size(y, mu, sigma);

      for (size_t n = 0; n < length(y); n++)
        if (value_of(y_vec[n]) <= 0)
          return LOG_ZERO;

      OperandsAndPartials<T_y, T_loc, T_scale>
        operands_and_partials(y, mu, sigma);

      using stan::math::square;
      using std::log;
      using stan::math::NEG_LOG_SQRT_TWO_PI;
      using std::log;


      VectorBuilder<include_summand<propto, T_scale>::value,
                    T_partials_return, T_scale> log_sigma(length(sigma));
      if (include_summand<propto, T_scale>::value) {
        for (size_t n = 0; n < length(sigma); n++)
          log_sigma[n] = log(value_of(sigma_vec[n]));
      }

      VectorBuilder<include_summand<propto, T_y, T_loc, T_scale>::value,
                    T_partials_return, T_scale> inv_sigma(length(sigma));
      VectorBuilder<include_summand<propto, T_y, T_loc, T_scale>::value,
                    T_partials_return, T_scale> inv_sigma_sq(length(sigma));
      if (include_summand<propto, T_y, T_loc, T_scale>::value) {
        for (size_t n = 0; n < length(sigma); n++)
          inv_sigma[n] = 1 / value_of(sigma_vec[n]);
      }
      if (include_summand<propto, T_y, T_loc, T_scale>::value) {
        for (size_t n = 0; n < length(sigma); n++)
          inv_sigma_sq[n] = inv_sigma[n] * inv_sigma[n];
      }

      VectorBuilder<include_summand<propto, T_y, T_loc, T_scale>::value,
                    T_partials_return, T_y> log_y(length(y));
      if (include_summand<propto, T_y, T_loc, T_scale>::value) {
        for (size_t n = 0; n < length(y); n++)
          log_y[n] = log(value_of(y_vec[n]));
      }

      VectorBuilder<!is_constant_struct<T_y>::value,
                    T_partials_return, T_y> inv_y(length(y));
      if (!is_constant_struct<T_y>::value) {
        for (size_t n = 0; n < length(y); n++)
          inv_y[n] = 1 / value_of(y_vec[n]);
      }

      if (include_summand<propto>::value)
        logp += N * NEG_LOG_SQRT_TWO_PI;

      for (size_t n = 0; n < N; n++) {
        const T_partials_return mu_dbl = value_of(mu_vec[n]);

        T_partials_return logy_m_mu(0);
        if (include_summand<propto, T_y, T_loc, T_scale>::value)
          logy_m_mu = log_y[n] - mu_dbl;

        T_partials_return logy_m_mu_sq = logy_m_mu * logy_m_mu;
        T_partials_return logy_m_mu_div_sigma(0);
        if (contains_nonconstant_struct<T_y, T_loc, T_scale>::value)
          logy_m_mu_div_sigma = logy_m_mu * inv_sigma_sq[n];


        // log probability
        if (include_summand<propto, T_scale>::value)
          logp -= log_sigma[n];
        if (include_summand<propto, T_y>::value)
          logp -= log_y[n];
        if (include_summand<propto, T_y, T_loc, T_scale>::value)
          logp -= 0.5 * logy_m_mu_sq * inv_sigma_sq[n];

        // gradients
        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] -= (1 + logy_m_mu_div_sigma) * inv_y[n];
        if (!is_constant_struct<T_loc>::value)
          operands_and_partials.d_x2[n] += logy_m_mu_div_sigma;
        if (!is_constant_struct<T_scale>::value)
          operands_and_partials.d_x3[n]
            += (logy_m_mu_div_sigma * logy_m_mu - 1) * inv_sigma[n];
      }
      return operands_and_partials.to_var(logp, y, mu, sigma);
    }
コード例 #6
0
ファイル: log2.hpp プロジェクト: Alienfeel/stan
 inline typename boost::math::tools::promote_args<T>::type
 log2(const T a) {
   using std::log;
   return log(a) / LOG_2;
 }
コード例 #7
0
ファイル: skew_normal_log.hpp プロジェクト: javaosos/stan
    typename return_type<T_y,T_loc,T_scale,T_shape>::type
    skew_normal_log(const T_y& y, const T_loc& mu, const T_scale& sigma, 
                    const T_shape& alpha) {
      static const char* function("stan::prob::skew_normal_log");
      typedef typename stan::partials_return_type<T_y,T_loc,
                                                  T_scale,T_shape>::type 
        T_partials_return;

      using std::log;
      using stan::is_constant_struct;
      using stan::math::check_positive;
      using stan::math::check_finite;
      using stan::math::check_not_nan;
      using stan::math::check_consistent_sizes;
      using stan::math::value_of;
      using stan::prob::include_summand;

      // check if any vectors are zero length
      if (!(stan::length(y) 
            && stan::length(mu) 
            && stan::length(sigma)
            && stan::length(alpha)))
        return 0.0;

      // set up return value accumulator
      T_partials_return logp(0.0);

      // validate args (here done over var, which should be OK)
      check_not_nan(function, "Random variable", y);
      check_finite(function, "Location parameter", mu);
      check_finite(function, "Shape parameter", alpha);
      check_positive(function, "Scale parameter", sigma);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Location parameter", mu,
                             "Scale parameter", sigma,
                             "Shape paramter", alpha);

      // check if no variables are involved and prop-to
      if (!include_summand<propto,T_y,T_loc,T_scale,T_shape>::value)
        return 0.0;
      
      // set up template expressions wrapping scalars into vector views
      agrad::OperandsAndPartials<T_y, T_loc, T_scale, T_shape> 
        operands_and_partials(y, mu, sigma, alpha);

      using boost::math::erfc;
      using boost::math::erf;

      VectorView<const T_y> y_vec(y);
      VectorView<const T_loc> mu_vec(mu);
      VectorView<const T_scale> sigma_vec(sigma);
      VectorView<const T_shape> alpha_vec(alpha);
      size_t N = max_size(y, mu, sigma, alpha);

      VectorBuilder<true, T_partials_return, T_scale> inv_sigma(length(sigma));
      VectorBuilder<include_summand<propto,T_scale>::value,
                    T_partials_return, T_scale> log_sigma(length(sigma));
      for (size_t i = 0; i < length(sigma); i++) {
        inv_sigma[i] = 1.0 / value_of(sigma_vec[i]);
        if (include_summand<propto,T_scale>::value)
          log_sigma[i] = log(value_of(sigma_vec[i]));
      }

      for (size_t n = 0; n < N; n++) {
        // pull out values of arguments
        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return mu_dbl = value_of(mu_vec[n]);
        const T_partials_return sigma_dbl = value_of(sigma_vec[n]);
        const T_partials_return alpha_dbl = value_of(alpha_vec[n]);

        // reusable subexpression values
        const T_partials_return y_minus_mu_over_sigma 
          = (y_dbl - mu_dbl) * inv_sigma[n];
        const double pi_dbl = stan::math::pi();

        // log probability
        if (include_summand<propto>::value)
          logp -=  0.5 * log(2.0 * pi_dbl);
        if (include_summand<propto, T_scale>::value)
          logp -= log(sigma_dbl);
        if (include_summand<propto,T_y, T_loc, T_scale>::value)
          logp -= y_minus_mu_over_sigma * y_minus_mu_over_sigma / 2.0;
        if (include_summand<propto,T_y,T_loc,T_scale,T_shape>::value)
          logp += log(erfc(-alpha_dbl * y_minus_mu_over_sigma 
                           / std::sqrt(2.0)));

        // gradients
        T_partials_return deriv_logerf 
          = 2.0 / std::sqrt(pi_dbl) 
          * exp(-alpha_dbl * y_minus_mu_over_sigma / std::sqrt(2.0) 
                * alpha_dbl * y_minus_mu_over_sigma / std::sqrt(2.0))
          / (1 + erf(alpha_dbl * y_minus_mu_over_sigma 
                     / std::sqrt(2.0)));
        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] 
            += -y_minus_mu_over_sigma / sigma_dbl 
            + deriv_logerf * alpha_dbl / (sigma_dbl * std::sqrt(2.0)) ;
        if (!is_constant_struct<T_loc>::value)
          operands_and_partials.d_x2[n] 
            += y_minus_mu_over_sigma / sigma_dbl 
            + deriv_logerf * -alpha_dbl / (sigma_dbl * std::sqrt(2.0));
        if (!is_constant_struct<T_scale>::value)
          operands_and_partials.d_x3[n] 
            += -1.0 / sigma_dbl 
            + y_minus_mu_over_sigma * y_minus_mu_over_sigma / sigma_dbl 
            - deriv_logerf * y_minus_mu_over_sigma * alpha_dbl 
            / (sigma_dbl * std::sqrt(2.0));
        if (!is_constant_struct<T_shape>::value)
          operands_and_partials.d_x4[n] 
            += deriv_logerf * y_minus_mu_over_sigma / std::sqrt(2.0);
      }
      return operands_and_partials.to_var(logp,y,mu,sigma,alpha);
    }
コード例 #8
0
ファイル: rgbdodometry.cpp プロジェクト: 2693/opencv
template <typename Scalar> Scalar log2(Scalar v) { using std::log; return log(v)/log(Scalar(2)); }
コード例 #9
0
ファイル: double_exponential.hpp プロジェクト: HerraHuu/stan
    typename return_type<T_y,T_loc,T_scale>::type
    double_exponential_ccdf_log(const T_y& y, const T_loc& mu, 
                               const T_scale& sigma) {
      static const char* function
        = "stan::prob::double_exponential_ccdf_log(%1%)";
      
      using stan::math::check_finite;
      using stan::math::check_not_nan;
      using stan::math::check_positive;
      using stan::math::check_consistent_sizes;
      using stan::math::value_of;

      double ccdf_log(0.0);

      // check if any vectors are zero length
      if (!(stan::length(y) 
            && stan::length(mu) 
            && stan::length(sigma)))
        return ccdf_log;

      if(!check_not_nan(function, y, "Random variable", &ccdf_log))
        return ccdf_log;
      if(!check_finite(function, mu, "Location parameter", &ccdf_log))
        return ccdf_log;
      if(!check_finite(function, sigma, "Scale parameter", &ccdf_log))
        return ccdf_log;
      if(!check_positive(function, sigma, "Scale parameter", &ccdf_log))
        return ccdf_log;
      if (!(check_consistent_sizes(function, y, mu, sigma,
                                   "Random variable", "Location parameter", 
                                   "Scale Parameter", &ccdf_log)))
        return ccdf_log;
      
      using std::log;
      using std::exp;

      agrad::OperandsAndPartials<T_y, T_loc, T_scale> 
        operands_and_partials(y, mu, sigma);

      VectorView<const T_y> y_vec(y);
      VectorView<const T_loc> mu_vec(mu);
      VectorView<const T_scale> sigma_vec(sigma);
      const double log_half = std::log(0.5);
      size_t N = max_size(y, mu, sigma);

      for (size_t n = 0; n < N; n++) {
        const double y_dbl = value_of(y_vec[n]);
        const double mu_dbl = value_of(mu_vec[n]);
        const double sigma_dbl = value_of(sigma_vec[n]);
        const double scaled_diff = (y_dbl - mu_dbl) / sigma_dbl;
        const double inv_sigma = 1.0 / sigma_dbl;
        if(y_dbl < mu_dbl) {
          //log ccdf
          ccdf_log += log(1.0 - 0.5 * exp(scaled_diff));

          //gradients
          const double rep_deriv = 1.0 / (2.0 * exp(-scaled_diff) - 1.0);
        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] -= rep_deriv * inv_sigma;
        if (!is_constant_struct<T_loc>::value)
          operands_and_partials.d_x2[n] += rep_deriv * inv_sigma;
        if (!is_constant_struct<T_scale>::value)
          operands_and_partials.d_x3[n] += rep_deriv * scaled_diff 
            * inv_sigma;
        }
        else {
          // log ccdf
          ccdf_log += log_half - scaled_diff;

          // gradients
          if (!is_constant_struct<T_y>::value)
            operands_and_partials.d_x1[n] -= inv_sigma;
          if (!is_constant_struct<T_loc>::value)
            operands_and_partials.d_x2[n] += inv_sigma;
          if (!is_constant_struct<T_scale>::value)
            operands_and_partials.d_x3[n] += scaled_diff * inv_sigma;
        }
      }
      return operands_and_partials.to_var(ccdf_log);
    }
コード例 #10
0
ファイル: log2.hpp プロジェクト: stan-dev/math
 /**
  * Returns the base two logarithm of the argument (C99, C++11).
  *
  * The function is defined by:
  *
  * <code>log2(a) = log(a) / std::log(2.0)</code>.
  *
  * @param[in] u argument
  * @return base two logarithm of argument
  */
 inline double log2(double u) {
   using std::log;
   return log(u) / LOG_2;
 }
コード例 #11
0
void BaseReverseMode<Base>::reverse_local_computation(size_t ind_num, size_t dep_num) {
    using std::sin;
    using std::cos;
    using std::acos;
    using std::sqrt;
    using std::pow;
    using std::log;
    using std::exp;

    DerivativeInfo<locint, Base> info;
    if (!trace) {
        warning_NoTraceSet();
    }
    if (ind_num != trace->get_num_ind()) {
        warning_NumberInconsistent("independent", ind_num, trace->get_num_ind());
    }
    if (dep_num != trace->get_num_dep()) {
        warning_NumberInconsistent("dependent", dep_num, trace->get_num_dep());
    }
    size_t ind_count = trace->get_num_ind();
    size_t dep_count = trace->get_num_dep();

    locint res;
    double coval;
    trace->init_reverse();
    opbyte op = trace->get_next_op_r();

    while (op != start_of_tape) {
        info.clear();
        info.opcode = op;
        switch (op) {
        case start_of_tape:
        case end_of_tape:
            break;
        case assign_ind:
            res = trace->get_next_loc_r();;
            trace->get_next_val_r();
            if (ind_count == 0) {
                // TODO(warning)
            }
            ind_count--;
            indep_index_map[res] = ind_count;
            break;
        case assign_dep:
            res = trace->get_next_loc_r();
            dep_value[res] = trace->get_next_val_r();
            init_dep_deriv(res);
            if (dep_count == 0) {
                // TODO(warning)
            }
            dep_count--;
            dep_index_map[res] = dep_count;
            break;
        case assign_param:
            info.r = trace->get_next_loc_r();
            trace->get_next_param_r();
            break;
        case assign_d:
            info.r = trace->get_next_loc_r();
            trace->get_next_coval_r();
            break;
        case assign_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.dx = 1.0;
            break;
        case comp_eq:
        case comp_lt:
            trace->get_next_loc_r();
            trace->get_next_loc_r();
            trace->get_next_coval_r();
            break;
        case eq_plus_a:
        case plus_a_a:
            info.r = trace->get_next_loc_r();
            info.y = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.dx = 1.0;
            info.dy = 1.0;
            PSEUDO_BINARY
            break;
        case eq_plus_d:
        case plus_d_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            trace->get_next_coval_r();
            info.dx = 1.0;
            break;
        case eq_minus_a:
        case minus_a_a:
            info.r = trace->get_next_loc_r();
            info.y = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.dx = 1.0;
            info.dy = -1.0;
            PSEUDO_BINARY
            break;
        case minus_d_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            trace->get_next_coval_r();
            info.dx = -1.0;
            break;
        case eq_mult_a:
        case mult_a_a:
            info.r = trace->get_next_loc_r();
            info.y = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vy = trace->get_next_val_r();
            info.vx = trace->get_next_val_r();
            info.dx = info.vy;
            info.dy = info.vx;
            info.pxy = 1.0;
            PSEUDO_BINARY
            break;
        case eq_mult_d:
        case mult_d_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.dx = trace->get_next_coval_r();
            break;
        case eq_div_a:
        case div_a_a:
            info.r = trace->get_next_loc_r();
            info.y = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vy = trace->get_next_val_r();
            info.vx = trace->get_next_val_r();
            info.dx = 1.0 / info.vy;
            info.dy = -info.vx / (info.vy*info.vy);
            info.pxy = -1.0 / (info.vy*info.vy);
            info.pyy = 2.0 * info.vx / (info.vy*info.vy*info.vy);
            info.pxyy = 2.0 / (info.vy * info.vy * info.vy);
            info.pyyy = -6.0 * info.vx / (info.vy*info.vy*info.vy*info.vy);
            PSEUDO_BINARY
            break;
        case div_d_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            coval = trace->get_next_coval_r();
            info.dx = -coval / (info.vx*info.vx);
            info.pxx = 2.0 * coval / (info.vx*info.vx*info.vx);
            info.pxxx = -6.0 * coval / (info.vx*info.vx*info.vx*info.vx);
            break;
        case sin_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            info.dx = cos(info.vx);
            info.pxx = -sin(info.vx);
            info.pxxx = -cos(info.vx);
            break;
        case cos_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            info.dx = -sin(info.vx);
            info.pxx = -cos(info.vx);
            info.pxxx = sin(info.vx);
            break;
        case asin_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            {
                Base t = sqrt(1.0 - info.vx * info.vx);
                info.dx = 1.0 / t;
                info.pxx = info.vx / (t * t * t);
                info.pxxx = (2.0*info.vx*info.vx+1.0) / (t*t*t*t*t);
            }
            break;
        case acos_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            {
                Base t = -sqrt(1.0 - info.vx * info.vx);
                info.dx = 1.0 / t;
                info.pxx = info.vx / (t * t * t);
                info.pxxx = (2.0*info.vx*info.vx+1.0) / (t*t*t*t*t);
            }
            break;
        case atan_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            {
                Base t = 1.0 + info.vx * info.vx;
                info.dx = 1.0 / t;
                info.pxx = -2.0 * info.vx / (t * t);
                info.pxxx = (6.0*info.vx*info.vx-2.0)/(t*t*t);
            }
            break;
        case sqrt_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            if (info.vx != 0.0) {
                info.dx = 0.5/sqrt(info.vx);
                info.pxx = -0.5 * info.dx / info.vx;
                info.pxxx = -1.5 * info.pxx / info.vx;
            }
            break;
        case exp_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            info.dx = exp(info.vx);
            info.pxx = info.dx;
            info.pxxx = info.dx;
            break;
        case log_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            info.dx = 1.0 / info.vx;
            info.pxx = - info.dx * info.dx;
            info.pxxx = -2.0 * info.pxx / info.vx;
            break;
        case pow_a_a:
            info.r = trace->get_next_loc_r();
            info.y = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vy = trace->get_next_val_r();
            info.vx = trace->get_next_val_r();
            {
                Base t = pow(info.vx, info.vy);
                info.dx = info.vy * t / info.vx;
                info.pxx = (info.vy - 1) * info.dx / info.vx;
                info.dy = log(info.vx) * t;
                info.pyy = log(info.vx) * info.dy;
                info.pxy = (info.vy * log(info.vx) + 1) * t / info.vx;
                info.pxxx = (info.vy - 2) * info.pxx / info.vx;
                info.pxxy = (info.vy-1)*info.pxy/info.vx + info.vy*t/(info.vx*info.vx);
                info.pxyy = info.dx*log(info.vx)*log(info.vx) + 2*log(info.vx)*t/info.vx;
                info.pyyy = log(info.vx) * info.pyy;
            }
            PSEUDO_BINARY
            break;
        case pow_a_d:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            coval = trace->get_next_coval_r();
            info.coval = coval;
            {
                Base t = pow(info.vx, coval);
                info.dx = coval * t / info.vx;
                info.pxx = (coval - 1) * info.dx / info.vx;
                info.pxxx = (coval - 2) * info.pxx / info.vx;
            }
            break;
        case pow_d_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            coval = trace->get_next_coval_r();
            info.coval = coval;
            {
                Base t = pow(coval, info.vx);
                info.dx = log(coval) * t;
                info.pxx = log(coval) * info.dx;
                info.pxxx = log(coval) * info.pxx;
            }
            break;
        case erf_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            info.dx = 2.0/sqrt(PI)*exp(-info.vx*info.vx);
            info.pxx = info.dx * (-2.0 * info.vx);
            info.pxxx = info.dx * (4.0 * info.vx * info.vx - 2);
            break;
        case fabs_a:
            info.r = trace->get_next_loc_r();
            info.x = trace->get_next_loc_r();
            info.vx = trace->get_next_val_r();
            if (info.vx > 0) {
                info.dx = 1.0;
            } else if (info.vx < 0) {
                info.dx = -1.0;
            } else {
                // TODO(muwang) : warning message
            }
            break;
        case rmpi_send:
        case rmpi_recv:
            break;
        default:
            warning_UnrecognizedOpcode((int)op);
        }
        // call to inherited virtual functions
        process_sac(info);

        op = trace->get_next_op_r();
    }
    // this is only for preaccumulation
    info.clear();
    info.opcode = op;
    process_sac(info);
    trace->end_reverse();
    return;
}
コード例 #12
0
ファイル: beta_ccdf_log.hpp プロジェクト: alyst/math
    typename return_type<T_y, T_scale_succ, T_scale_fail>::type
    beta_ccdf_log(const T_y& y, const T_scale_succ& alpha,
                  const T_scale_fail& beta) {
      typedef typename stan::partials_return_type<T_y, T_scale_succ,
                                                  T_scale_fail>::type
        T_partials_return;

      // Size checks
      if ( !( stan::length(y) && stan::length(alpha)
              && stan::length(beta) ) )
        return 0.0;

      // Error checks
      static const char* function("stan::math::beta_cdf");

      using stan::math::check_positive_finite;
      using stan::math::check_not_nan;
      using stan::math::check_nonnegative;
      using stan::math::check_less_or_equal;
      using boost::math::tools::promote_args;
      using stan::math::check_consistent_sizes;
      using stan::math::value_of;

      T_partials_return ccdf_log(0.0);

      check_positive_finite(function, "First shape parameter", alpha);
      check_positive_finite(function, "Second shape parameter", beta);
      check_not_nan(function, "Random variable", y);
      check_nonnegative(function, "Random variable", y);
      check_less_or_equal(function, "Random variable", y, 1);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "First shape parameter", alpha,
                             "Second shape parameter", beta);

      // Wrap arguments in vectors
      VectorView<const T_y> y_vec(y);
      VectorView<const T_scale_succ> alpha_vec(alpha);
      VectorView<const T_scale_fail> beta_vec(beta);
      size_t N = max_size(y, alpha, beta);

      OperandsAndPartials<T_y, T_scale_succ, T_scale_fail>
        operands_and_partials(y, alpha, beta);

      // Compute CDF and its gradients
      using stan::math::inc_beta;
      using stan::math::digamma;
      using stan::math::lbeta;
      using std::pow;
      using std::exp;
      using std::log;
      using std::exp;

      // Cache a few expensive function calls if alpha or beta is a parameter
      VectorBuilder<contains_nonconstant_struct<T_scale_succ,
                                                T_scale_fail>::value,
                    T_partials_return, T_scale_succ, T_scale_fail>
        digamma_alpha_vec(max_size(alpha, beta));
      VectorBuilder<contains_nonconstant_struct<T_scale_succ,
                                                T_scale_fail>::value,
                    T_partials_return, T_scale_succ, T_scale_fail>
        digamma_beta_vec(max_size(alpha, beta));
      VectorBuilder<contains_nonconstant_struct<T_scale_succ,
                                                T_scale_fail>::value,
                    T_partials_return, T_scale_succ, T_scale_fail>
        digamma_sum_vec(max_size(alpha, beta));

      if (contains_nonconstant_struct<T_scale_succ, T_scale_fail>::value) {
        for (size_t i = 0; i < N; i++) {
          const T_partials_return alpha_dbl = value_of(alpha_vec[i]);
          const T_partials_return beta_dbl = value_of(beta_vec[i]);

          digamma_alpha_vec[i] = digamma(alpha_dbl);
          digamma_beta_vec[i] = digamma(beta_dbl);
          digamma_sum_vec[i] = digamma(alpha_dbl + beta_dbl);
        }
      }

      // Compute vectorized CDFLog and gradient
      for (size_t n = 0; n < N; n++) {
        // Pull out values
        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return alpha_dbl = value_of(alpha_vec[n]);
        const T_partials_return beta_dbl = value_of(beta_vec[n]);
        const T_partials_return betafunc_dbl = exp(lbeta(alpha_dbl, beta_dbl));

        // Compute
        const T_partials_return Pn = 1.0 - inc_beta(alpha_dbl, beta_dbl, y_dbl);

        ccdf_log += log(Pn);

        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] -= pow(1-y_dbl, beta_dbl-1)
            * pow(y_dbl, alpha_dbl-1) / betafunc_dbl / Pn;

        T_partials_return g1 = 0;
        T_partials_return g2 = 0;

        if (contains_nonconstant_struct<T_scale_succ, T_scale_fail>::value) {
          stan::math::grad_reg_inc_beta(g1, g2, alpha_dbl, beta_dbl, y_dbl,
                                        digamma_alpha_vec[n],
                                        digamma_beta_vec[n],
                                        digamma_sum_vec[n],
                                        betafunc_dbl);
        }
        if (!is_constant_struct<T_scale_succ>::value)
          operands_and_partials.d_x2[n] -= g1 / Pn;
        if (!is_constant_struct<T_scale_fail>::value)
          operands_and_partials.d_x3[n] -= g2 / Pn;
      }

      return operands_and_partials.to_var(ccdf_log, y, alpha, beta);
    }
コード例 #13
0
ファイル: ib_fabric.cpp プロジェクト: nateucar/libibautils
bool fabric_t::build_lid_map(bool determine_lmc)
{
  ///Always start clean
  clear_lidmap();
  
  {
    const lmc_t max_lmc_lid = lmc > 0 ? (1 << lmc) - 1 : 0;
    
    /**
    * Walk every entity and build lid map
    */
    for(
      entities_t::iterator 
        itr = entities.begin(),
        eitr = entities.end();
      itr != eitr;
      ++itr
    )
    {
      assert(itr->second.lid() > 0);
      entity_t &entity = itr->second;
      const lid_t blid = entity.lid();
      assert(blid > 0);
      
      if(entity.get_type() == port_type::HCA)
        for(lmc_t i = 0; i <= max_lmc_lid; ++i)
        {
#ifndef NDEBUG
          std::cerr << "set HCA lid " << entity.label() << "(" << itr->first << std::hex << ") = " << regex::string_cast_uint(blid + i) << std::endl;
#endif
          assert(lidmap.find(blid + i) == lidmap.end());
          lidmap[blid + i] = &entity;
        }
      else 
        if(entity.get_type() == port_type::TCA) 
        { ///Switchs do not get a second LID
#ifndef NDEBUG
          {
            entitiesmap_lid_t::iterator itr = lidmap.find(blid);
            if(itr != lidmap.end())
            {
              std::cerr << "attempting fabric lmc = " << regex::string_cast_uint(lmc) << std::endl;
              std::cerr << "found existing port " << itr->second->label() <<  " on lid " << blid << std::endl;
              std::cerr << "was going to set port " << entity.label() <<  " on lid " << blid << std::endl;
              abort();
            }
          }
          std::cerr << "set TCA lid " << entity.label() << "(" << itr->first << std::hex << ") = " << regex::string_cast_uint(blid) << std::endl;
#endif
          lidmap[blid] = &entity;
        }
      else
        abort(); ///unknown port type?
    }
  }
  
  /**
    * attempt to determine LMC value of the subnet
    * this can be done with reasonable accuracy since
    * all lmc lid values are sequential for lmc > 0
    * 
    * this is the brute force solution O(ports)*lmc
    * basically, walk every port and see if there are any other lid+lmc
    * until the smalled lid, lid+lmc*, lid sequence is found 
    * then use lmc=log2(found)
    * 
    * LIDs = BASELID to BASELID + (2^LMC - 1)
    */
  if(determine_lmc)
  {
    using std::log;
    const lmc_t current_lmc = lmc;
    assert(portmap.size() > 1);
    
    ///Start off assuming max LMC value
    lmc_t max_lmc_lid = (1 << MAX_LMC_VALUE) - 1;
    entitiesmap_lid_t::const_iterator lid_end = lidmap.end();
    
    for(
      portmap_guidport_t::const_iterator
        itr = portmap.begin(),
        eitr = portmap.end();
      itr != eitr && max_lmc_lid > 0;
      ++itr
    )
      ///Only search LIDs of HCAs
      if(itr->second->type == port_type::HCA)
      {
#ifndef NDEBUG
        std::cerr << "search port " << itr->second->label() << std::endl;
#endif

        ///walk until highest seen lmc value offset
        for(lmc_t i = 1; i <= max_lmc_lid; ++i)
        {
          assert(itr->second->lid > 0);
          assert(lidmap.find(itr->second->lid) != lid_end);
          
          ///is there lid on base lid + lmc offset
          if(lidmap.find(itr->second->lid + i) != lid_end)
          {
#ifndef NDEBUG
            std::cerr << "found base lid " << itr->second->lid << " + " << regex::string_cast_uint(i) << " = " << itr->second->lid + i << " => collision\n";
#endif
            ///found collision, found new max lid offset
            max_lmc_lid = i - 1;
            break;
          }
#ifndef NDEBUG
          else
            std::cerr << "found base lid " << itr->second->lid << " + " << regex::string_cast_uint(i) << " = " << itr->second->lid + i << " => no collision\n";
#endif
        }
      }
    
#if __cplusplus <= 199711L
    lmc = (log(max_lmc_lid) / log(2)) + 1;
#else
    lmc = std::log2(max_lmc_lid) + 1;
#endif
    
    assert(lmc <= MAX_LMC_VALUE);
#ifndef NDEBUG
    std::cerr << "fabric lmc = " << regex::string_cast_uint(lmc) << " max lmc offset = " << regex::string_cast_uint(max_lmc_lid) << std::endl;
#endif
    
    ///LMC is new number so lid map is incomplete
    if(current_lmc != lmc)
      return build_lid_map(false);
  }
  
  return true;
}
コード例 #14
0
    typename return_type<T_y,T_loc,T_scale>::type
    normal_log(const T_y& y, const T_loc& mu, const T_scale& sigma) {
      static const char* function = "stan::prob::normal_log(%1%)";

      using std::log;
      using stan::is_constant_struct;
      using stan::math::check_positive;
      using stan::math::check_finite;
      using stan::math::check_not_nan;
      using stan::math::check_consistent_sizes;
      using stan::math::value_of;
      using stan::prob::include_summand;

      // check if any vectors are zero length
      if (!(stan::length(y) 
            && stan::length(mu) 
            && stan::length(sigma)))
        return 0.0;

      // set up return value accumulator
      double logp(0.0);

      // validate args (here done over var, which should be OK)
      if (!check_not_nan(function, y, "Random variable", &logp))
        return logp;
      if (!check_finite(function, mu, "Location parameter", 
                        &logp))
        return logp;
      if (!check_positive(function, sigma, "Scale parameter", 
                          &logp))
        return logp;
      if (!(check_consistent_sizes(function,
                                   y,mu,sigma,
                                   "Random variable","Location parameter","Scale parameter",
                                   &logp)))
        return logp;

      // check if no variables are involved and prop-to
      if (!include_summand<propto,T_y,T_loc,T_scale>::value)
        return 0.0;
      
      // set up template expressions wrapping scalars into vector views
      agrad::OperandsAndPartials<T_y, T_loc, T_scale> operands_and_partials(y, mu, sigma);

      VectorView<const T_y> y_vec(y);
      VectorView<const T_loc> mu_vec(mu);
      VectorView<const T_scale> sigma_vec(sigma);
      size_t N = max_size(y, mu, sigma);

      DoubleVectorView<true,is_vector<T_scale>::value> inv_sigma(length(sigma));
      DoubleVectorView<include_summand<propto,T_scale>::value,is_vector<T_scale>::value> log_sigma(length(sigma));
      for (size_t i = 0; i < length(sigma); i++) {
        inv_sigma[i] = 1.0 / value_of(sigma_vec[i]);
        if (include_summand<propto,T_scale>::value)
          log_sigma[i] = log(value_of(sigma_vec[i]));
      }

      for (size_t n = 0; n < N; n++) {
        // pull out values of arguments
        const double y_dbl = value_of(y_vec[n]);
        const double mu_dbl = value_of(mu_vec[n]);
      
        // reusable subexpression values
        const double y_minus_mu_over_sigma 
          = (y_dbl - mu_dbl) * inv_sigma[n];
        const double y_minus_mu_over_sigma_squared 
          = y_minus_mu_over_sigma * y_minus_mu_over_sigma;

        static double NEGATIVE_HALF = - 0.5;

        // log probability
        if (include_summand<propto>::value)
          logp += NEG_LOG_SQRT_TWO_PI;
        if (include_summand<propto,T_scale>::value)
          logp -= log_sigma[n];
        if (include_summand<propto,T_y,T_loc,T_scale>::value)
          logp += NEGATIVE_HALF * y_minus_mu_over_sigma_squared;

        // gradients
        double scaled_diff = inv_sigma[n] * y_minus_mu_over_sigma;
        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] -= scaled_diff;
        if (!is_constant_struct<T_loc>::value)
          operands_and_partials.d_x2[n] += scaled_diff;
        if (!is_constant_struct<T_scale>::value)
          operands_and_partials.d_x3[n] 
            += -inv_sigma[n] + inv_sigma[n] * y_minus_mu_over_sigma_squared;
      }
      return operands_and_partials.to_var(logp);
    }
コード例 #15
0
ファイル: mixture.cpp プロジェクト: cajal/cmt
bool CMT::Mixture::train(
	const MatrixXd& data,
	const Parameters& parameters,
	const Component::Parameters& componentParameters)
{
	if(data.rows() != dim())
		throw Exception("Data has wrong dimensionality.");

	if(parameters.initialize && !initialized())
		initialize(data, parameters, componentParameters);

	ArrayXXd logJoint(numComponents(), data.cols());
	Array<double, Dynamic, 1> postSum;
	Array<double, 1, Dynamic> logLik;
	ArrayXXd post;
	ArrayXXd weights;
	double avgLogLoss = numeric_limits<double>::infinity();
	double avgLogLossNew;

	for(int i = 0; i < parameters.maxIter; ++i) {
		// compute joint probability of data and assignments (E)
		#pragma omp parallel for
		for(int k = 0; k < numComponents(); ++k)
			logJoint.row(k) = mComponents[k]->logLikelihood(data) + log(mPriors[k]);

		// compute normalized posterior (E)
		logLik = logSumExp(logJoint);

		// average negative log-likelihood in bits per component
		avgLogLossNew = -logLik.mean() / log(2.) / dim();

		if(parameters.verbosity > 0)
			cout << setw(6) << i << setw(14) << setprecision(7) << avgLogLossNew << endl;

		// test for convergence
		if(avgLogLoss - avgLogLossNew < parameters.threshold)
			return true;
		avgLogLoss = avgLogLossNew;

		// compute normalized posterior (E)
		post = (logJoint.rowwise() - logLik).exp();
		postSum = post.rowwise().sum();
		weights = post.colwise() / postSum;

		// optimize prior weights (M)
		if(parameters.trainPriors) {
			mPriors = postSum / data.cols() + parameters.regularizePriors;
			mPriors /= mPriors.sum();
		}

		// optimize components (M)
		if(parameters.trainComponents) {
			#pragma omp parallel for
			for(int k = 0; k < numComponents(); ++k)
				mComponents[k]->train(data, weights.row(k), componentParameters);
		} else {
			return true;
		}
	}

	if(parameters.verbosity > 0)
		cout << setw(6) << parameters.maxIter << setw(14) << setprecision(7) << evaluate(data) << endl;

	return false;
}
コード例 #16
0
ファイル: rayleigh_log.hpp プロジェクト: alyst/math
    typename return_type<T_y, T_scale>::type
    rayleigh_log(const T_y& y, const T_scale& sigma) {
      static const char* function("stan::math::rayleigh_log");
      typedef typename stan::partials_return_type<T_y, T_scale>::type
        T_partials_return;

      using std::log;
      using stan::is_constant_struct;
      using stan::math::check_positive;
      using stan::math::check_not_nan;
      using stan::math::check_consistent_sizes;
      using stan::math::value_of;
      using stan::math::include_summand;
      using std::log;

      // check if any vectors are zero length
      if (!(stan::length(y) && stan::length(sigma)))
        return 0.0;

      // set up return value accumulator
      T_partials_return logp(0.0);

      // validate args (here done over var, which should be OK)
      check_not_nan(function, "Random variable", y);
      check_positive(function, "Scale parameter", sigma);
      check_positive(function, "Random variable", y);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Scale parameter", sigma);

      // check if no variables are involved and prop-to
      if (!include_summand<propto, T_y, T_scale>::value)
        return 0.0;

      // set up template expressions wrapping scalars into vector views
      OperandsAndPartials<T_y, T_scale> operands_and_partials(y, sigma);

      VectorView<const T_y> y_vec(y);
      VectorView<const T_scale> sigma_vec(sigma);
      size_t N = max_size(y, sigma);

      VectorBuilder<true, T_partials_return, T_scale> inv_sigma(length(sigma));
      VectorBuilder<include_summand<propto, T_scale>::value,
                    T_partials_return, T_scale> log_sigma(length(sigma));
      for (size_t i = 0; i < length(sigma); i++) {
        inv_sigma[i] = 1.0 / value_of(sigma_vec[i]);
        if (include_summand<propto, T_scale>::value)
          log_sigma[i] = log(value_of(sigma_vec[i]));
      }

      for (size_t n = 0; n < N; n++) {
        // pull out values of arguments
        const T_partials_return y_dbl = value_of(y_vec[n]);

        // reusable subexpression values
        const T_partials_return y_over_sigma = y_dbl * inv_sigma[n];

        static double NEGATIVE_HALF = -0.5;

        // log probability
        if (include_summand<propto, T_scale>::value)
          logp -= 2.0 * log_sigma[n];
        if (include_summand<propto, T_y>::value)
          logp += log(y_dbl);
        // if (include_summand<propto, T_y, T_scale>::value)
        logp += NEGATIVE_HALF * y_over_sigma * y_over_sigma;

        // gradients
        T_partials_return scaled_diff = inv_sigma[n] * y_over_sigma;
        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] += 1.0 / y_dbl - scaled_diff;
        if (!is_constant_struct<T_scale>::value)
          operands_and_partials.d_x2[n]
            += y_over_sigma * scaled_diff - 2.0 * inv_sigma[n];
      }
      return operands_and_partials.to_var(logp, y, sigma);
    }
コード例 #17
0
ファイル: mixture.cpp プロジェクト: cajal/cmt
bool CMT::Mixture::train(
	const MatrixXd& data,
	const MatrixXd& dataValid,
	const Parameters& parameters,
	const Component::Parameters& componentParameters)
{
	if(parameters.initialize && !initialized())
		initialize(data, parameters, componentParameters);

	ArrayXXd logJoint(numComponents(), data.cols());
	Array<double, Dynamic, 1> postSum;
	Array<double, 1, Dynamic> logLik;
	ArrayXXd post;
	ArrayXXd weights;

	// training and validation log-loss for checking convergence
	double avgLogLoss = numeric_limits<double>::infinity();
	double avgLogLossNew;
	double avgLogLossValid = evaluate(dataValid);
	double avgLogLossValidNew = avgLogLossValid;
	int counter = 0;

	// backup model parameters
	VectorXd priors = mPriors;
	vector<Component*> components;

	for(int k = 0; k < numComponents(); ++k)
		components.push_back(mComponents[k]->copy());

	for(int i = 0; i < parameters.maxIter; ++i) {
		// compute joint probability of data and assignments (E)
		#pragma omp parallel for
		for(int k = 0; k < numComponents(); ++k)
			logJoint.row(k) = mComponents[k]->logLikelihood(data) + log(mPriors[k]);

		// compute normalized posterior (E)
		logLik = logSumExp(logJoint);

		// average negative log-likelihood in bits per component
		avgLogLossNew = -logLik.mean() / log(2.) / dim();

		if(parameters.verbosity > 0) {
			if(i % parameters.valIter == 0) {
				// print training and validation error
				cout << setw(6) << i;
				cout << setw(14) << setprecision(7) << avgLogLossNew;
				cout << setw(14) << setprecision(7) << avgLogLossValidNew << endl;
			} else {
				// print training error
				cout << setw(6) << i << setw(14) << setprecision(7) << avgLogLossNew << endl;
			}
		}

		// test for convergence
		if(avgLogLoss - avgLogLossNew < parameters.threshold)
			return true;
		avgLogLoss = avgLogLossNew;

		// compute normalized posterior (E)
		post = (logJoint.rowwise() - logLik).exp();
		postSum = post.rowwise().sum();
		weights = post.colwise() / postSum;

		// optimize prior weights (M)
		if(parameters.trainPriors) {
			mPriors = postSum / data.cols() + parameters.regularizePriors;
			mPriors /= mPriors.sum();
		}

		// optimize components (M)
		if(parameters.trainComponents) {
			#pragma omp parallel for
			for(int k = 0; k < numComponents(); ++k)
				mComponents[k]->train(data, weights.row(k), componentParameters);
		} else {
			return true;
		}

		if((i + 1) % parameters.valIter == 0) {
			// check validation error
			avgLogLossValidNew = evaluate(dataValid);

			if(avgLogLossValidNew < avgLogLossValid) {
				// backup new found model parameters
				priors = mPriors;
				for(int k = 0; k < numComponents(); ++k)
					*components[k] = *mComponents[k];
				
				avgLogLossValid = avgLogLossValidNew;
			} else {
				counter++;

				if(parameters.valLookAhead > 0 && counter >= parameters.valLookAhead) {
					// set parameters to best parameters found during training
					mPriors = priors;

					for(int k = 0; k < numComponents(); ++k) {
						*mComponents[k] = *components[k];
						delete components[k];
					}

					return true;
				}
			}
		}
	}

	if(parameters.verbosity > 0)
		cout << setw(6) << parameters.maxIter << setw(11) << setprecision(5) << evaluate(data) << endl;

	return false;
}
コード例 #18
0
    typename return_type<T_y,T_loc,T_scale>::type
    double_exponential_log(const T_y& y, 
                           const T_loc& mu, const T_scale& sigma) {
      static const char* function("stan::prob::double_exponential_log");
      typedef typename stan::partials_return_type<T_y,T_loc,T_scale>::type 
        T_partials_return;
      
      using stan::is_constant_struct;
      using stan::math::check_finite;
      using stan::math::check_positive_finite;
      using stan::math::check_consistent_sizes;
      using stan::math::value_of;
      using stan::prob::include_summand;
      using std::log;
      using std::fabs;
      using stan::math::sign;

      // check if any vectors are zero length
      if (!(stan::length(y) 
            && stan::length(mu) 
            && stan::length(sigma)))
        return 0.0;

      // set up return value accumulator
      T_partials_return logp(0.0);
      check_finite(function, "Random variable", y);
      check_finite(function, "Location parameter", mu);
      check_positive_finite(function, "Scale parameter", sigma);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Location parameter", mu,
                             "Shape parameter", sigma);
      
      // check if no variables are involved and prop-to
      if (!include_summand<propto,T_y,T_loc,T_scale>::value)
        return 0.0;

      // set up template expressions wrapping scalars into vector views
      VectorView<const T_y> y_vec(y);
      VectorView<const T_loc> mu_vec(mu);
      VectorView<const T_scale> sigma_vec(sigma);
      size_t N = max_size(y, mu, sigma);
      agrad::OperandsAndPartials<T_y,T_loc,T_scale> 
        operands_and_partials(y, mu, sigma);

      VectorBuilder<include_summand<propto,T_y,T_loc,T_scale>::value,
                    T_partials_return, T_scale> inv_sigma(length(sigma));
      VectorBuilder<!is_constant_struct<T_scale>::value,
                    T_partials_return, T_scale> 
        inv_sigma_squared(length(sigma));
      VectorBuilder<include_summand<propto,T_scale>::value,
                    T_partials_return, T_scale> log_sigma(length(sigma));
      for (size_t i = 0; i < length(sigma); i++) {
        const T_partials_return sigma_dbl = value_of(sigma_vec[i]);
        if (include_summand<propto,T_y,T_loc,T_scale>::value)
          inv_sigma[i] = 1.0 / sigma_dbl;
        if (include_summand<propto,T_scale>::value) 
          log_sigma[i] = log(value_of(sigma_vec[i]));
        if (!is_constant_struct<T_scale>::value) 
          inv_sigma_squared[i] = inv_sigma[i] * inv_sigma[i];
      }


      for (size_t n = 0; n < N; n++) {
        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return mu_dbl = value_of(mu_vec[n]);
  
        // reusable subexpressions values
        const T_partials_return y_m_mu = y_dbl - mu_dbl;
        const T_partials_return fabs_y_m_mu = fabs(y_m_mu);

        // log probability
        if (include_summand<propto>::value)
          logp += NEG_LOG_TWO;
        if (include_summand<propto,T_scale>::value)
          logp -= log_sigma[n];
        if (include_summand<propto,T_y,T_loc,T_scale>::value)
          logp -= fabs_y_m_mu * inv_sigma[n];
  
        // gradients
        T_partials_return sign_y_m_mu_times_inv_sigma(0);
        if (contains_nonconstant_struct<T_y,T_loc>::value)
          sign_y_m_mu_times_inv_sigma = sign(y_m_mu) * inv_sigma[n];
        if (!is_constant_struct<T_y>::value) {
          operands_and_partials.d_x1[n] -= sign_y_m_mu_times_inv_sigma;
        }
        if (!is_constant_struct<T_loc>::value) {
          operands_and_partials.d_x2[n] += sign_y_m_mu_times_inv_sigma;
        }
        if (!is_constant_struct<T_scale>::value)
          operands_and_partials.d_x3[n] += -inv_sigma[n] + fabs_y_m_mu 
            * inv_sigma_squared[n];
      }
      return operands_and_partials.to_var(logp,y,mu,sigma);
    }
コード例 #19
0
ファイル: pyramid.new.cpp プロジェクト: Nuos/Image-Morphing
T log2(const T &v)/*{{{*/
{
    using std::log;
    return log(v)/log(2.0);
}/*}}}*/
コード例 #20
0
    typename return_type<T_shape, T_inv_scale>::type
    neg_binomial_cdf_log(const T_n& n, const T_shape& alpha,
                         const T_inv_scale& beta) {
      static const char* function("stan::math::neg_binomial_cdf_log");
      typedef typename stan::partials_return_type<T_n, T_shape,
                                                  T_inv_scale>::type
        T_partials_return;

      using stan::math::check_positive_finite;
      using stan::math::check_nonnegative;
      using stan::math::check_consistent_sizes;
      using stan::math::include_summand;

      // Ensure non-zero arugment lengths
      if (!(stan::length(n) && stan::length(alpha) && stan::length(beta)))
        return 0.0;

      T_partials_return P(0.0);

      // Validate arguments
      check_positive_finite(function, "Shape parameter", alpha);
      check_positive_finite(function, "Inverse scale parameter", beta);
      check_consistent_sizes(function,
                             "Failures variable", n,
                             "Shape parameter", alpha,
                             "Inverse scale parameter", beta);

      // Wrap arguments in vector views
      VectorView<const T_n> n_vec(n);
      VectorView<const T_shape> alpha_vec(alpha);
      VectorView<const T_inv_scale> beta_vec(beta);
      size_t size = max_size(n, alpha, beta);

      // Compute vectorized cdf_log and gradient
      using stan::math::value_of;
      using stan::math::inc_beta;
      using stan::math::digamma;
      using stan::math::lbeta;
      using std::exp;
      using std::pow;
      using std::log;
      using std::exp;


      OperandsAndPartials<T_shape, T_inv_scale>
        operands_and_partials(alpha, beta);

      // Explicit return for extreme values
      // The gradients are technically ill-defined, but treated as zero
      for (size_t i = 0; i < stan::length(n); i++) {
        if (value_of(n_vec[i]) < 0)
          return operands_and_partials.value(stan::math::negative_infinity());
      }

      // Cache a few expensive function calls if alpha is a parameter
      VectorBuilder<!is_constant_struct<T_shape>::value,
                    T_partials_return, T_shape>
        digammaN_vec(stan::length(alpha));
      VectorBuilder<!is_constant_struct<T_shape>::value,
                    T_partials_return, T_shape>
        digammaAlpha_vec(stan::length(alpha));
      VectorBuilder<!is_constant_struct<T_shape>::value,
                    T_partials_return, T_shape>
        digammaSum_vec(stan::length(alpha));

      if (!is_constant_struct<T_shape>::value) {
        for (size_t i = 0; i < stan::length(alpha); i++) {
          const T_partials_return n_dbl = value_of(n_vec[i]);
          const T_partials_return alpha_dbl = value_of(alpha_vec[i]);

          digammaN_vec[i] = digamma(n_dbl + 1);
          digammaAlpha_vec[i] = digamma(alpha_dbl);
          digammaSum_vec[i] = digamma(n_dbl + alpha_dbl + 1);
        }
      }

      for (size_t i = 0; i < size; i++) {
        // Explicit results for extreme values
        // The gradients are technically ill-defined, but treated as zero
        if (value_of(n_vec[i]) == std::numeric_limits<int>::max())
          return operands_and_partials.value(0.0);

        const T_partials_return n_dbl = value_of(n_vec[i]);
        const T_partials_return alpha_dbl = value_of(alpha_vec[i]);
        const T_partials_return beta_dbl = value_of(beta_vec[i]);
        const T_partials_return p_dbl = beta_dbl / (1.0 + beta_dbl);
        const T_partials_return d_dbl = 1.0 / ( (1.0 + beta_dbl)
                                                * (1.0 + beta_dbl) );
        const T_partials_return Pi = inc_beta(alpha_dbl, n_dbl + 1.0, p_dbl);
        const T_partials_return beta_func = exp(lbeta(n_dbl + 1, alpha_dbl));


        P += log(Pi);

        if (!is_constant_struct<T_shape>::value) {
          T_partials_return g1 = 0;
          T_partials_return g2 = 0;

          stan::math::grad_reg_inc_beta(g1, g2, alpha_dbl,
                                        n_dbl + 1, p_dbl,
                                        digammaAlpha_vec[i],
                                        digammaN_vec[i],
                                        digammaSum_vec[i],
                                        beta_func);
          operands_and_partials.d_x1[i] += g1 / Pi;
        }
        if (!is_constant_struct<T_inv_scale>::value)
          operands_and_partials.d_x2[i]  += d_dbl * pow(1-p_dbl, n_dbl)
            * pow(p_dbl, alpha_dbl-1) / beta_func / Pi;
      }

      return operands_and_partials.value(P);
    }
コード例 #21
0
ファイル: transform_test.cpp プロジェクト: HerraHuu/stan
TEST(prob_transform, positive_f) {
  EXPECT_FLOAT_EQ(log(0.5), stan::prob::positive_free(0.5));
}
コード例 #22
0
ファイル: main.cpp プロジェクト: kemaleren/sequtil
int main( int argc, const char * argv[] )
{
    args_t args = args_t( argc, argv );
    coverage_t coverage;
    vector< cov_t > variants;
    vector< pair< int, int > > data;

    // accumulate the data at each position in a linked list
    {
        cov_citer cit;
        bam1_t * in_bam = bam_init1();

        while ( args.bamin->next( in_bam ) ) {
            aligned_t read( in_bam );
            coverage.include( read );
        }

        for ( cit = coverage.begin(); cit != coverage.end(); ++cit ) {
            int cov = 0;

            for ( obs_citer it = cit->obs.begin(); it != cit->obs.end(); ++it )
                cov += it->second;
           
            for ( obs_citer it = cit->obs.begin(); it != cit->obs.end(); ++it )
                if ( it->second )
                    data.push_back( make_pair( cov, it->second ) );

#if 0
            obs_citer it = cit->obs.begin();
            int cov = 0, maj;

            if ( it == cit->obs.end() )
                continue;

            maj = it->second;
            cov += maj;

            for ( ++it; it != cit->obs.end(); ++it ) {
                if ( it->second > maj )
                    maj = it->second;
                cov += it->second;
            }

            data.push_back( make_pair( cov, maj ) );
#endif
        }

        bam_destroy1( in_bam );
    }

    // learn a joint multi-binomial model for the mutation rate classes
    {
        cov_iter cit;
        double lg_L, aicc, bg, lg_bg, lg_invbg;
        rateclass_t rc( data );
        vector< pair< double, double > > params;

        rc( lg_L, aicc, params );

        bg = params[ 0 ].second;
        lg_bg = log( bg );
        lg_invbg = log( 1.0 - bg );

        params_json_dump( stderr, lg_L, aicc, params );

        // cerr << "background: " << bg << endl;

        // determine which variants are above background and those which are not
        for ( cit = coverage.begin(); cit != coverage.end(); ++cit ) {
            if ( cit->op == INS )
                continue;

            int cov = 0;

            for ( obs_citer it = cit->obs.begin(); it != cit->obs.end(); ++it )
                cov += it->second;

            for ( obs_iter it = cit->obs.begin(); it != cit->obs.end(); ++it ) {
                const double p = prob_background( lg_bg, lg_invbg, cov, it->second );
                if ( p < args.cutoff ) {
                    cout << cit->col << "\t" << cov << "\t" << it->second;
                    for ( unsigned i = 0; i < it->first.size(); ++i )
                        cout << bits2nuc( it->first[ i ] );
                    cout << ":" << p << endl;
                    it->second = 1;
                }
                else {
                    it->second = 0;
                }
            }

#if 0
            variants.push_back( *cit );
#endif
        }
    }

    return 0;

    // write out the input reads, but only with "real" variants this time
    {
        bam1_t * const in_bam = bam_init1();

        if ( !args.bamin->seek0() ) {
            cerr << "unable to seek( 0 )" << endl;
            exit( 1 );
        }

        if ( !args.bamout->write_header( args.bamin->hdr ) ) {
            cerr << "error writing out BAM header" << endl;
            exit( 1 );
        }

        while ( args.bamin->next( in_bam ) ) {
            aligned_t read( in_bam );

            bam1_t * const out_bam = punchout_read( in_bam, variants, read );

            if ( !out_bam->core.l_qseq )
                continue;

            if ( !args.bamout->write( out_bam ) ) {
                cerr << "error writing out read" << endl;
                exit( 1 );
            }

            bam_destroy1( out_bam );
        }

        bam_destroy1( in_bam );
    }

    return 0;
}
コード例 #23
0
    typename return_type<T_y, T_dof, T_scale>::type
    scaled_inv_chi_square_ccdf_log(const T_y& y, const T_dof& nu,
                                   const T_scale& s) {
      typedef typename stan::partials_return_type<T_y, T_dof, T_scale>::type
        T_partials_return;

      if (!(stan::length(y) && stan::length(nu) && stan::length(s)))
        return 0.0;

      static const char* function("scaled_inv_chi_square_ccdf_log");

      using std::exp;

      T_partials_return P(0.0);

      check_not_nan(function, "Random variable", y);
      check_nonnegative(function, "Random variable", y);
      check_positive_finite(function, "Degrees of freedom parameter", nu);
      check_positive_finite(function, "Scale parameter", s);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Degrees of freedom parameter", nu,
                             "Scale parameter", s);

      VectorView<const T_y> y_vec(y);
      VectorView<const T_dof> nu_vec(nu);
      VectorView<const T_scale> s_vec(s);
      size_t N = max_size(y, nu, s);

      OperandsAndPartials<T_y, T_dof, T_scale>
        operands_and_partials(y, nu, s);

      // Explicit return for extreme values
      // The gradients are technically ill-defined, but treated as zero
      for (size_t i = 0; i < stan::length(y); i++) {
        if (value_of(y_vec[i]) == 0)
          return operands_and_partials.value(0.0);
      }

      using std::exp;
      using std::pow;
      using std::log;

      VectorBuilder<!is_constant_struct<T_dof>::value,
                    T_partials_return, T_dof> gamma_vec(stan::length(nu));
      VectorBuilder<!is_constant_struct<T_dof>::value,
                    T_partials_return, T_dof> digamma_vec(stan::length(nu));

      if (!is_constant_struct<T_dof>::value) {
        for (size_t i = 0; i < stan::length(nu); i++) {
          const T_partials_return half_nu_dbl = 0.5 * value_of(nu_vec[i]);
          gamma_vec[i] = tgamma(half_nu_dbl);
          digamma_vec[i] = digamma(half_nu_dbl);
        }
      }

      for (size_t n = 0; n < N; n++) {
        // Explicit results for extreme values
        // The gradients are technically ill-defined, but treated as zero
        if (value_of(y_vec[n]) == std::numeric_limits<double>::infinity()) {
          return operands_and_partials.value(negative_infinity());
        }

        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return y_inv_dbl = 1.0 / y_dbl;
        const T_partials_return half_nu_dbl = 0.5 * value_of(nu_vec[n]);
        const T_partials_return s_dbl = value_of(s_vec[n]);
        const T_partials_return half_s2_overx_dbl = 0.5 * s_dbl * s_dbl
          * y_inv_dbl;
        const T_partials_return half_nu_s2_overx_dbl
          = 2.0 * half_nu_dbl * half_s2_overx_dbl;

        const T_partials_return Pn = gamma_p(half_nu_dbl,
                                             half_nu_s2_overx_dbl);
        const T_partials_return gamma_p_deriv = exp(-half_nu_s2_overx_dbl)
          * pow(half_nu_s2_overx_dbl, half_nu_dbl-1) / tgamma(half_nu_dbl);

        P += log(Pn);

        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] -= half_nu_s2_overx_dbl * y_inv_dbl
            * gamma_p_deriv / Pn;
        if (!is_constant_struct<T_dof>::value)
          operands_and_partials.d_x2[n]
            -= (0.5 * grad_reg_inc_gamma(half_nu_dbl,
                                         half_nu_s2_overx_dbl,
                                         gamma_vec[n],
                                         digamma_vec[n])
                - half_s2_overx_dbl * gamma_p_deriv)
            / Pn;
        if (!is_constant_struct<T_scale>::value)
          operands_and_partials.d_x3[n] += 2.0 * half_nu_dbl * s_dbl * y_inv_dbl
            * gamma_p_deriv / Pn;
      }
      return operands_and_partials.value(P);
    }
コード例 #24
0
ファイル: student_t_cdf_log.hpp プロジェクト: aseyboldt/math
    typename return_type<T_y, T_dof, T_loc, T_scale>::type
    student_t_cdf_log(const T_y& y, const T_dof& nu, const T_loc& mu,
                      const T_scale& sigma) {
      typedef typename
        stan::partials_return_type<T_y, T_dof, T_loc, T_scale>::type
        T_partials_return;

      // Size checks
      if (!(stan::length(y) && stan::length(nu) && stan::length(mu)
            && stan::length(sigma)))
        return 0.0;

      static const char* function("stan::math::student_t_cdf_log");

      using stan::math::check_positive_finite;
      using stan::math::check_finite;
      using stan::math::check_not_nan;
      using stan::math::check_consistent_sizes;
      using stan::math::value_of;
      using std::exp;

      T_partials_return P(0.0);

      check_not_nan(function, "Random variable", y);
      check_positive_finite(function, "Degrees of freedom parameter", nu);
      check_finite(function, "Location parameter", mu);
      check_positive_finite(function, "Scale parameter", sigma);

      // Wrap arguments in vectors
      VectorView<const T_y> y_vec(y);
      VectorView<const T_dof> nu_vec(nu);
      VectorView<const T_loc> mu_vec(mu);
      VectorView<const T_scale> sigma_vec(sigma);
      size_t N = max_size(y, nu, mu, sigma);

      OperandsAndPartials<T_y, T_dof, T_loc, T_scale>
        operands_and_partials(y, nu, mu, sigma);

      // Explicit return for extreme values
      // The gradients are technically ill-defined, but treated as zero
      for (size_t i = 0; i < stan::length(y); i++) {
        if (value_of(y_vec[i]) == -std::numeric_limits<double>::infinity())
          return operands_and_partials.value(stan::math::negative_infinity());
      }

      using stan::math::digamma;
      using stan::math::lbeta;
      using stan::math::inc_beta;
      using std::pow;
      using std::exp;
      using std::log;

      // Cache a few expensive function calls if nu is a parameter
      T_partials_return digammaHalf = 0;

      VectorBuilder<!is_constant_struct<T_dof>::value,
                    T_partials_return, T_dof>
        digamma_vec(stan::length(nu));
      VectorBuilder<!is_constant_struct<T_dof>::value,
                    T_partials_return, T_dof>
        digammaNu_vec(stan::length(nu));
      VectorBuilder<!is_constant_struct<T_dof>::value,
                    T_partials_return, T_dof>
        digammaNuPlusHalf_vec(stan::length(nu));

      if (!is_constant_struct<T_dof>::value) {
        digammaHalf = digamma(0.5);

        for (size_t i = 0; i < stan::length(nu); i++) {
          const T_partials_return nu_dbl = value_of(nu_vec[i]);

          digammaNu_vec[i] = digamma(0.5 * nu_dbl);
          digammaNuPlusHalf_vec[i] = digamma(0.5 + 0.5 * nu_dbl);
        }
      }

      // Compute vectorized cdf_log and gradient
      for (size_t n = 0; n < N; n++) {
        // Explicit results for extreme values
        // The gradients are technically ill-defined, but treated as zero
        if (value_of(y_vec[n]) == std::numeric_limits<double>::infinity()) {
          continue;
        }

        const T_partials_return sigma_inv = 1.0 / value_of(sigma_vec[n]);
        const T_partials_return t = (value_of(y_vec[n]) - value_of(mu_vec[n]))
          * sigma_inv;
        const T_partials_return nu_dbl = value_of(nu_vec[n]);
        const T_partials_return q = nu_dbl / (t * t);
        const T_partials_return r = 1.0 / (1.0 + q);
        const T_partials_return J = 2 * r * r * q / t;
        const T_partials_return betaNuHalf = exp(lbeta(0.5, 0.5 * nu_dbl));
        T_partials_return zJacobian = t > 0 ? - 0.5 : 0.5;

        if (q < 2) {
          T_partials_return z
            = inc_beta(0.5 * nu_dbl, (T_partials_return)0.5, 1.0 - r);
          const T_partials_return Pn = t > 0 ? 1.0 - 0.5 * z : 0.5 * z;
          const T_partials_return d_ibeta = pow(r, -0.5)
            * pow(1.0 - r, 0.5*nu_dbl - 1) / betaNuHalf;

          P += log(Pn);

          if (!is_constant_struct<T_y>::value)
            operands_and_partials.d_x1[n]
              += - zJacobian * d_ibeta * J * sigma_inv / Pn;

          if (!is_constant_struct<T_dof>::value) {
            T_partials_return g1 = 0;
            T_partials_return g2 = 0;

            stan::math::grad_reg_inc_beta(g1, g2, 0.5 * nu_dbl,
                                          (T_partials_return)0.5, 1.0 - r,
                                          digammaNu_vec[n], digammaHalf,
                                          digammaNuPlusHalf_vec[n],
                                          betaNuHalf);

            operands_and_partials.d_x2[n]
              += zJacobian * (d_ibeta * (r / t) * (r / t) + 0.5 * g1) / Pn;
          }

          if (!is_constant_struct<T_loc>::value)
            operands_and_partials.d_x3[n]
              += zJacobian * d_ibeta * J * sigma_inv / Pn;
          if (!is_constant_struct<T_scale>::value)
            operands_and_partials.d_x4[n]
              += zJacobian * d_ibeta * J * sigma_inv * t / Pn;

        } else {
          T_partials_return z = 1.0 - inc_beta((T_partials_return)0.5,
                                               0.5*nu_dbl, r);
          zJacobian *= -1;

          const T_partials_return Pn = t > 0 ? 1.0 - 0.5 * z : 0.5 * z;

          T_partials_return d_ibeta = pow(1.0-r, 0.5*nu_dbl-1) * pow(r, -0.5)
            / betaNuHalf;

          P += log(Pn);

          if (!is_constant_struct<T_y>::value)
            operands_and_partials.d_x1[n]
              += zJacobian * d_ibeta * J * sigma_inv / Pn;

          if (!is_constant_struct<T_dof>::value) {
            T_partials_return g1 = 0;
            T_partials_return g2 = 0;

            stan::math::grad_reg_inc_beta(g1, g2, (T_partials_return)0.5,
                                          0.5 * nu_dbl, r,
                                          digammaHalf, digammaNu_vec[n],
                                          digammaNuPlusHalf_vec[n],
                                          betaNuHalf);

            operands_and_partials.d_x2[n]
              += zJacobian * (- d_ibeta * (r / t) * (r / t) + 0.5 * g2) / Pn;
          }

          if (!is_constant_struct<T_loc>::value)
            operands_and_partials.d_x3[n]
              += - zJacobian * d_ibeta * J * sigma_inv / Pn;
          if (!is_constant_struct<T_scale>::value)
            operands_and_partials.d_x4[n]
              += - zJacobian * d_ibeta * J * sigma_inv * t / Pn;
        }
      }

    return operands_and_partials.value(P);
    }
コード例 #25
0
ファイル: inv_chi_square_cdf_log.hpp プロジェクト: alyst/math
    typename return_type<T_y, T_dof>::type
    inv_chi_square_cdf_log(const T_y& y, const T_dof& nu) {
      typedef typename stan::partials_return_type<T_y, T_dof>::type
        T_partials_return;

      // Size checks
      if ( !( stan::length(y) && stan::length(nu) ) ) return 0.0;

      // Error checks
      static const char* function("stan::math::inv_chi_square_cdf_log");

      using stan::math::check_positive_finite;
      using stan::math::check_not_nan;
      using stan::math::check_consistent_sizes;
      using stan::math::check_nonnegative;
      using boost::math::tools::promote_args;
      using stan::math::value_of;
      using std::exp;

      T_partials_return P(0.0);

      check_positive_finite(function, "Degrees of freedom parameter", nu);
      check_not_nan(function, "Random variable", y);
      check_nonnegative(function, "Random variable", y);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Degrees of freedom parameter", nu);

      // Wrap arguments in vectors
      VectorView<const T_y> y_vec(y);
      VectorView<const T_dof> nu_vec(nu);
      size_t N = max_size(y, nu);

      OperandsAndPartials<T_y, T_dof> operands_and_partials(y, nu);

      // Explicit return for extreme values
      // The gradients are technically ill-defined, but treated as zero

      for (size_t i = 0; i < stan::length(y); i++)
        if (value_of(y_vec[i]) == 0)
          return operands_and_partials.to_var(stan::math::negative_infinity(),
                                              y, nu);

      // Compute cdf_log and its gradients
      using stan::math::gamma_q;
      using stan::math::digamma;
      using boost::math::tgamma;
      using std::exp;
      using std::pow;
      using std::log;

      // Cache a few expensive function calls if nu is a parameter
      VectorBuilder<!is_constant_struct<T_dof>::value,
                    T_partials_return, T_dof> gamma_vec(stan::length(nu));
      VectorBuilder<!is_constant_struct<T_dof>::value,
                    T_partials_return, T_dof> digamma_vec(stan::length(nu));

      if (!is_constant_struct<T_dof>::value)  {
        for (size_t i = 0; i < stan::length(nu); i++) {
          const T_partials_return nu_dbl = value_of(nu_vec[i]);
          gamma_vec[i] = tgamma(0.5 * nu_dbl);
          digamma_vec[i] = digamma(0.5 * nu_dbl);
        }
      }

      // Compute vectorized cdf_log and gradient
      for (size_t n = 0; n < N; n++) {
        // Explicit results for extreme values
        // The gradients are technically ill-defined, but treated as zero
        if (value_of(y_vec[n]) == std::numeric_limits<double>::infinity()) {
          continue;
        }

        // Pull out values
        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return y_inv_dbl = 1.0 / y_dbl;
        const T_partials_return nu_dbl = value_of(nu_vec[n]);

        // Compute
        const T_partials_return Pn = gamma_q(0.5 * nu_dbl, 0.5 * y_inv_dbl);

        P += log(Pn);

        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] += 0.5 * y_inv_dbl * y_inv_dbl
            * exp(-0.5*y_inv_dbl) * pow(0.5*y_inv_dbl, 0.5*nu_dbl-1)
            / tgamma(0.5*nu_dbl) / Pn;
        if (!is_constant_struct<T_dof>::value)
          operands_and_partials.d_x2[n]
            += 0.5 * stan::math::grad_reg_inc_gamma(0.5 * nu_dbl,
                                                    0.5 * y_inv_dbl,
                                                    gamma_vec[n],
                                                    digamma_vec[n]) / Pn;
      }

      return operands_and_partials.to_var(P, y, nu);
    }
コード例 #26
0
ファイル: skew_normal_ccdf_log.hpp プロジェクト: alyst/math
    typename return_type<T_y, T_loc, T_scale, T_shape>::type
    skew_normal_ccdf_log(const T_y& y, const T_loc& mu, const T_scale& sigma,
                         const T_shape& alpha) {
      static const char* function("stan::math::skew_normal_ccdf_log");
      typedef typename stan::partials_return_type<T_y, T_loc, T_scale,
                                                  T_shape>::type
        T_partials_return;

      using stan::math::check_positive;
      using stan::math::check_finite;
      using stan::math::check_not_nan;
      using stan::math::check_consistent_sizes;
      using stan::math::owens_t;
      using stan::math::value_of;

      T_partials_return ccdf_log(0.0);

      // check if any vectors are zero length
      if (!(stan::length(y)
            && stan::length(mu)
            && stan::length(sigma)
            && stan::length(alpha)))
        return ccdf_log;

      check_not_nan(function, "Random variable", y);
      check_finite(function, "Location parameter", mu);
      check_not_nan(function, "Scale parameter", sigma);
      check_positive(function, "Scale parameter", sigma);
      check_finite(function, "Shape parameter", alpha);
      check_not_nan(function, "Shape parameter", alpha);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Location parameter", mu,
                             "Scale parameter", sigma,
                             "Shape paramter", alpha);

      OperandsAndPartials<T_y, T_loc, T_scale, T_shape>
        operands_and_partials(y, mu, sigma, alpha);

      using stan::math::SQRT_2;
      using stan::math::pi;
      using std::log;
      using std::exp;

      VectorView<const T_y> y_vec(y);
      VectorView<const T_loc> mu_vec(mu);
      VectorView<const T_scale> sigma_vec(sigma);
      VectorView<const T_shape> alpha_vec(alpha);
      size_t N = max_size(y, mu, sigma, alpha);
      const double SQRT_TWO_OVER_PI = std::sqrt(2.0 / stan::math::pi());

      for (size_t n = 0; n < N; n++) {
        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return mu_dbl = value_of(mu_vec[n]);
        const T_partials_return sigma_dbl = value_of(sigma_vec[n]);
        const T_partials_return alpha_dbl = value_of(alpha_vec[n]);
        const T_partials_return alpha_dbl_sq = alpha_dbl * alpha_dbl;
        const T_partials_return diff = (y_dbl - mu_dbl) / sigma_dbl;
        const T_partials_return diff_sq = diff * diff;
        const T_partials_return scaled_diff =  diff / SQRT_2;
        const T_partials_return scaled_diff_sq =  diff_sq * 0.5;
        const T_partials_return ccdf_log_ = 1.0 - 0.5 * erfc(-scaled_diff)
          + 2 * owens_t(diff, alpha_dbl);

        // ccdf_log
        ccdf_log += log(ccdf_log_);

        // gradients
        const T_partials_return deriv_erfc = SQRT_TWO_OVER_PI * 0.5
          * exp(-scaled_diff_sq) / sigma_dbl;
        const T_partials_return deriv_owens = erf(alpha_dbl * scaled_diff)
          * exp(-scaled_diff_sq) / SQRT_TWO_OVER_PI / (-2.0 * pi()) / sigma_dbl;
        const T_partials_return rep_deriv = (-2.0 * deriv_owens + deriv_erfc)
          / ccdf_log_;

        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] -= rep_deriv;
        if (!is_constant_struct<T_loc>::value)
          operands_and_partials.d_x2[n] += rep_deriv;
        if (!is_constant_struct<T_scale>::value)
          operands_and_partials.d_x3[n] += rep_deriv * diff;
        if (!is_constant_struct<T_shape>::value)
          operands_and_partials.d_x4[n] -= -2.0 * exp(-0.5 * diff_sq
                                                      * (1.0 + alpha_dbl_sq))
            / ((1 + alpha_dbl_sq) * 2.0 * pi()) / ccdf_log_;
      }

      return operands_and_partials.to_var(ccdf_log, y, mu, sigma, alpha);
    }
コード例 #27
0
//
inline float degreeToRadian(float deg){ return deg/180*M_PI; }
const float a = 6378137;       // semi-major axis of the ellipsoid
const float e = 0.08181919106; // first eccentricity of the ellipsoid
const float lc = degreeToRadian(3.f);
const float l0 = degreeToRadian(3.f);
const float phi1 = degreeToRadian(44.f); // 1st automecoic parallel
const float phi2 = degreeToRadian(49.f); // 2nd automecoic parallel
const float phi0 = degreeToRadian(46.5f);// latitude of origin
const float X0 =  700000; // x coordinate at origin
const float Y0 = 6600000; // y coordinate at origin
// Normals
const float gN1 = a/sqrt(1-e*e*sin(phi1)*sin(phi1));
const float gN2 = a/sqrt(1-e*e*sin(phi2)*sin(phi2));
// Isometric latitudes
const float gl1=log(tan(M_PI/4+phi1/2)*pow((1-e*sin(phi1))/(1+e*sin(phi1)),e/2));
const float gl2=log(tan(M_PI/4+phi2/2)*pow((1-e*sin(phi2))/(1+e*sin(phi2)),e/2));
const float gl0=log(tan(M_PI/4+phi0/2)*pow((1-e*sin(phi0))/(1+e*sin(phi0)),e/2));
// Projection exponent
const float n = (log((gN2*cos(phi2))/(gN1*cos(phi1))))/(gl1-gl2);
// Projection constant
const float c = ((gN1*cos(phi1))/n)*exp(n*gl1);
// Coordinate
const float ys = Y0 + c*exp(-n*gl0);

// Convert geographic coordinates (latitude, longitude in degrees) into
// cartesian coordinates (in kilometers) using the Lambert 93 projection.
pair<float,float> geoToLambert93(float latitude,float longitude)
{
    float phi = degreeToRadian(latitude);
    float l   = degreeToRadian(longitude);
コード例 #28
0
ファイル: gamma_log.hpp プロジェクト: stan-dev/math
    typename return_type<T_y, T_shape, T_inv_scale>::type
    gamma_log(const T_y& y, const T_shape& alpha, const T_inv_scale& beta) {
      static const char* function("gamma_log");
      typedef typename stan::partials_return_type<T_y, T_shape,
                                                  T_inv_scale>::type
        T_partials_return;

      using stan::is_constant_struct;

      if (!(stan::length(y)
            && stan::length(alpha)
            && stan::length(beta)))
        return 0.0;

      T_partials_return logp(0.0);

      check_not_nan(function, "Random variable", y);
      check_positive_finite(function, "Shape parameter", alpha);
      check_positive_finite(function, "Inverse scale parameter", beta);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Shape parameter", alpha,
                             "Inverse scale parameter", beta);

      if (!include_summand<propto, T_y, T_shape, T_inv_scale>::value)
        return 0.0;

      VectorView<const T_y> y_vec(y);
      VectorView<const T_shape> alpha_vec(alpha);
      VectorView<const T_inv_scale> beta_vec(beta);

      for (size_t n = 0; n < length(y); n++) {
        const T_partials_return y_dbl = value_of(y_vec[n]);
        if (y_dbl < 0)
          return LOG_ZERO;
      }

      size_t N = max_size(y, alpha, beta);
      OperandsAndPartials<T_y, T_shape, T_inv_scale>
        operands_and_partials(y, alpha, beta);

      using boost::math::lgamma;
      using boost::math::digamma;
      using std::log;

      VectorBuilder<include_summand<propto, T_y, T_shape>::value,
                    T_partials_return, T_y> log_y(length(y));
      if (include_summand<propto, T_y, T_shape>::value) {
        for (size_t n = 0; n < length(y); n++) {
          if (value_of(y_vec[n]) > 0)
            log_y[n] = log(value_of(y_vec[n]));
        }
      }

      VectorBuilder<include_summand<propto, T_shape>::value,
                    T_partials_return, T_shape> lgamma_alpha(length(alpha));
      VectorBuilder<!is_constant_struct<T_shape>::value,
                    T_partials_return, T_shape> digamma_alpha(length(alpha));
      for (size_t n = 0; n < length(alpha); n++) {
        if (include_summand<propto, T_shape>::value)
          lgamma_alpha[n] = lgamma(value_of(alpha_vec[n]));
        if (!is_constant_struct<T_shape>::value)
          digamma_alpha[n] = digamma(value_of(alpha_vec[n]));
      }

      VectorBuilder<include_summand<propto, T_shape, T_inv_scale>::value,
                    T_partials_return, T_inv_scale> log_beta(length(beta));
      if (include_summand<propto, T_shape, T_inv_scale>::value) {
        for (size_t n = 0; n < length(beta); n++)
          log_beta[n] = log(value_of(beta_vec[n]));
      }

      for (size_t n = 0; n < N; n++) {
        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return alpha_dbl = value_of(alpha_vec[n]);
        const T_partials_return beta_dbl = value_of(beta_vec[n]);

        if (include_summand<propto, T_shape>::value)
          logp -= lgamma_alpha[n];
        if (include_summand<propto, T_shape, T_inv_scale>::value)
          logp += alpha_dbl * log_beta[n];
        if (include_summand<propto, T_y, T_shape>::value)
          logp += (alpha_dbl-1.0) * log_y[n];
        if (include_summand<propto, T_y, T_inv_scale>::value)
          logp -= beta_dbl * y_dbl;

        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] += (alpha_dbl-1)/y_dbl - beta_dbl;
        if (!is_constant_struct<T_shape>::value)
          operands_and_partials.d_x2[n] += -digamma_alpha[n] + log_beta[n]
            + log_y[n];
        if (!is_constant_struct<T_inv_scale>::value)
          operands_and_partials.d_x3[n] += alpha_dbl / beta_dbl - y_dbl;
      }
      return operands_and_partials.value(logp);
    }
コード例 #29
0
ファイル: gamma_ccdf_log.hpp プロジェクト: stan-dev/math
    typename return_type<T_y, T_shape, T_inv_scale>::type
    gamma_ccdf_log(const T_y& y, const T_shape& alpha,
                   const T_inv_scale& beta) {
      if (!(stan::length(y) && stan::length(alpha) && stan::length(beta)))
        return 0.0;

      typedef typename stan::partials_return_type<T_y, T_shape,
                                                  T_inv_scale>::type
        T_partials_return;

      static const char* function("gamma_ccdf_log");

      using boost::math::tools::promote_args;
      using std::exp;

      T_partials_return P(0.0);

      check_positive_finite(function, "Shape parameter", alpha);
      check_positive_finite(function, "Scale parameter", beta);
      check_not_nan(function, "Random variable", y);
      check_nonnegative(function, "Random variable", y);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "Shape parameter", alpha,
                             "Scale Parameter", beta);

      VectorView<const T_y> y_vec(y);
      VectorView<const T_shape> alpha_vec(alpha);
      VectorView<const T_inv_scale> beta_vec(beta);
      size_t N = max_size(y, alpha, beta);

      OperandsAndPartials<T_y, T_shape, T_inv_scale>
        operands_and_partials(y, alpha, beta);

      // Explicit return for extreme values
      // The gradients are technically ill-defined, but treated as zero
      for (size_t i = 0; i < stan::length(y); i++) {
        if (value_of(y_vec[i]) == 0)
          return operands_and_partials.value(0.0);
      }

      using boost::math::tgamma;
      using std::exp;
      using std::pow;
      using std::log;

      VectorBuilder<!is_constant_struct<T_shape>::value,
                    T_partials_return, T_shape> gamma_vec(stan::length(alpha));
      VectorBuilder<!is_constant_struct<T_shape>::value,
                    T_partials_return, T_shape>
        digamma_vec(stan::length(alpha));

      if (!is_constant_struct<T_shape>::value) {
        for (size_t i = 0; i < stan::length(alpha); i++) {
          const T_partials_return alpha_dbl = value_of(alpha_vec[i]);
          gamma_vec[i] = tgamma(alpha_dbl);
          digamma_vec[i] = digamma(alpha_dbl);
        }
      }

      for (size_t n = 0; n < N; n++) {
        // Explicit results for extreme values
        // The gradients are technically ill-defined, but treated as zero
        if (value_of(y_vec[n]) == std::numeric_limits<double>::infinity())
          return operands_and_partials.value(negative_infinity());

        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return alpha_dbl = value_of(alpha_vec[n]);
        const T_partials_return beta_dbl = value_of(beta_vec[n]);

        const T_partials_return Pn = gamma_q(alpha_dbl, beta_dbl * y_dbl);

        P += log(Pn);

        if (!is_constant_struct<T_y>::value)
          operands_and_partials.d_x1[n] -= beta_dbl * exp(-beta_dbl * y_dbl)
            * pow(beta_dbl * y_dbl, alpha_dbl-1) / tgamma(alpha_dbl) / Pn;
        if (!is_constant_struct<T_shape>::value)
          operands_and_partials.d_x2[n]
            += grad_reg_inc_gamma(alpha_dbl, beta_dbl
                                  * y_dbl, gamma_vec[n],
                                  digamma_vec[n]) / Pn;
        if (!is_constant_struct<T_inv_scale>::value)
          operands_and_partials.d_x3[n] -= y_dbl * exp(-beta_dbl * y_dbl)
            * pow(beta_dbl * y_dbl, alpha_dbl-1) / tgamma(alpha_dbl) / Pn;
      }
      return operands_and_partials.value(P);
    }
コード例 #30
0
ファイル: nonlinearities.cpp プロジェクト: cajal/cmt
double CMT::LogisticFunction::inverse(double data) const {
	return log((data - mEpsilon / 2.) / (1. - data - mEpsilon / 2.));
}