www.pudn.com > CsharpSVM.rar > svm.cs, change:2004-06-14,size:72931b
/*
* Conversion notes:
* Using JLCA 3.0, the only problem was with the save and loads. I changed them both to be
* StreamR/W around a FileStream. Originally, the save was a BinaryWriter over a FileStream
* and the Reader was a StreamReader over another StreamReader.
* In the Java code, it's a DataOutputStream around a FileOutputStream and a BufferedReader
* around a FileReader.
*/
using System;
namespace libsvm
{
//
// Kernel Cache
//
// l is the number of total data items
// size is the cache size limit in bytes
//
class Cache
{
//UPGRADE_NOTE: Final was removed from the declaration of 'l '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private int l;
private int size;
//UPGRADE_NOTE: Field 'EnclosingInstance' was added to class 'head_t' to access its enclosing instance. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1019_3"'
private sealed class head_t
{
public head_t(Cache enclosingInstance)
{
InitBlock(enclosingInstance);
}
private void InitBlock(Cache enclosingInstance)
{
this.enclosingInstance = enclosingInstance;
}
private Cache enclosingInstance;
public Cache Enclosing_Instance
{
get
{
return enclosingInstance;
}
}
internal head_t prev, next; // a cicular list
internal float[] data;
internal int len; // data[0,len) is cached in this entry
}
//UPGRADE_NOTE: Final was removed from the declaration of 'head '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private head_t[] head;
private head_t lru_head;
internal Cache(int l_, int size_)
{
l = l_;
size = size_;
head = new head_t[l];
for (int i = 0; i < l; i++)
head[i] = new head_t(this);
size /= 4;
size -= l * (16 / 4); // sizeof(head_t) == 16
lru_head = new head_t(this);
lru_head.next = lru_head.prev = lru_head;
}
private void lru_delete(head_t h)
{
// delete from current location
h.prev.next = h.next;
h.next.prev = h.prev;
}
private void lru_insert(head_t h)
{
// insert to last position
h.next = lru_head;
h.prev = lru_head.prev;
h.prev.next = h;
h.next.prev = h;
}
// request data [0,len)
// return some position p where [p,len) need to be filled
// (p >= len if nothing needs to be filled)
// java: simulate pointer using single-element array
internal virtual int get_data(int index, float[][] data, int len)
{
head_t h = head[index];
if (h.len > 0)
lru_delete(h);
int more = len - h.len;
if (more > 0)
{
// free old space
while (size < more)
{
head_t old = lru_head.next;
lru_delete(old);
size += old.len;
old.data = null;
old.len = 0;
}
// allocate new space
float[] new_data = new float[len];
if (h.data != null)
Array.Copy(h.data, 0, new_data, 0, h.len);
h.data = new_data;
size -= more;
do
{
int _ = h.len; h.len = len; len = _;
}
while (false);
}
lru_insert(h);
data[0] = h.data;
return len;
}
internal virtual void swap_index(int i, int j)
{
if (i == j)
return ;
if (head[i].len > 0)
lru_delete(head[i]);
if (head[j].len > 0)
lru_delete(head[j]);
do
{
float[] _ = head[i].data; head[i].data = head[j].data; head[j].data = _;
}
while (false);
do
{
int _ = head[i].len; head[i].len = head[j].len; head[j].len = _;
}
while (false);
if (head[i].len > 0)
lru_insert(head[i]);
if (head[j].len > 0)
lru_insert(head[j]);
if (i > j)
do
{
int _ = i; i = j; j = _;
}
while (false);
for (head_t h = lru_head.next; h != lru_head; h = h.next)
{
if (h.len > i)
{
if (h.len > j)
do
{
float _ = h.data[i]; h.data[i] = h.data[j]; h.data[j] = _;
}
while (false);
else
{
// give up
lru_delete(h);
size += h.len;
h.data = null;
h.len = 0;
}
}
}
}
}
//
// Kernel evaluation
//
// the static method k_function is for doing single kernel evaluation
// the constructor of Kernel prepares to calculate the l*l kernel matrix
// the member function get_Q is for getting one column from the Q Matrix
//
abstract class Kernel
{
private svm_node[][] x;
//UPGRADE_NOTE: Final was removed from the declaration of 'x_square '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private double[] x_square;
// svm_parameter
//UPGRADE_NOTE: Final was removed from the declaration of 'kernel_type '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private int kernel_type;
//UPGRADE_NOTE: Final was removed from the declaration of 'degree '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private double degree;
//UPGRADE_NOTE: Final was removed from the declaration of 'gamma '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private double gamma;
//UPGRADE_NOTE: Final was removed from the declaration of 'coef0 '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private double coef0;
internal abstract float[] get_Q(int column, int len);
internal virtual void swap_index(int i, int j)
{
do
{
svm_node[] _ = x[i]; x[i] = x[j]; x[j] = _;
}
while (false);
if (x_square != null)
do
{
double _ = x_square[i]; x_square[i] = x_square[j]; x_square[j] = _;
}
while (false);
}
private static double tanh(double x)
{
double e = System.Math.Exp(x);
return 1.0 - 2.0 / (e * e + 1);
}
internal virtual double kernel_function(int i, int j)
{
switch (kernel_type)
{
case svm_parameter.LINEAR:
return dot(x[i], x[j]);
case svm_parameter.POLY:
return System.Math.Pow(gamma * dot(x[i], x[j]) + coef0, degree);
case svm_parameter.RBF:
return System.Math.Exp((- gamma) * (x_square[i] + x_square[j] - 2 * dot(x[i], x[j])));
case svm_parameter.SIGMOID:
return tanh(gamma * dot(x[i], x[j]) + coef0);
default:
return 0; // java
}
}
internal Kernel(int l, svm_node[][] x_, svm_parameter param)
{
this.kernel_type = param.kernel_type;
this.degree = param.degree;
this.gamma = param.gamma;
this.coef0 = param.coef0;
x = (svm_node[][]) x_.Clone();
if (kernel_type == svm_parameter.RBF)
{
x_square = new double[l];
for (int i = 0; i < l; i++)
x_square[i] = dot(x[i], x[i]);
}
else
x_square = null;
}
internal static double dot(svm_node[] x, svm_node[] y)
{
double sum = 0;
int xlen = x.Length;
int ylen = y.Length;
int i = 0;
int j = 0;
while (i < xlen && j < ylen)
{
if (x[i].index == y[j].index)
sum += x[i++].value_Renamed * y[j++].value_Renamed;
else
{
if (x[i].index > y[j].index)
++j;
else
++i;
}
}
return sum;
}
internal static double k_function(svm_node[] x, svm_node[] y, svm_parameter param)
{
switch (param.kernel_type)
{
case svm_parameter.LINEAR:
return dot(x, y);
case svm_parameter.POLY:
return System.Math.Pow(param.gamma * dot(x, y) + param.coef0, param.degree);
case svm_parameter.RBF:
{
double sum = 0;
int xlen = x.Length;
int ylen = y.Length;
int i = 0;
int j = 0;
while (i < xlen && j < ylen)
{
if (x[i].index == y[j].index)
{
double d = x[i++].value_Renamed - y[j++].value_Renamed;
sum += d * d;
}
else if (x[i].index > y[j].index)
{
sum += y[j].value_Renamed * y[j].value_Renamed;
++j;
}
else
{
sum += x[i].value_Renamed * x[i].value_Renamed;
++i;
}
}
while (i < xlen)
{
sum += x[i].value_Renamed * x[i].value_Renamed;
++i;
}
while (j < ylen)
{
sum += y[j].value_Renamed * y[j].value_Renamed;
++j;
}
return System.Math.Exp((- param.gamma) * sum);
}
case svm_parameter.SIGMOID:
return tanh(param.gamma * dot(x, y) + param.coef0);
default:
return 0; // java
}
}
}
// Generalized SMO+SVMlight algorithm
// Solves:
//
// min 0.5(\alpha^T Q \alpha) + b^T \alpha
//
// y^T \alpha = \delta
// y_i = +1 or -1
// 0 <= alpha_i <= Cp for y_i = 1
// 0 <= alpha_i <= Cn for y_i = -1
//
// Given:
//
// Q, b, y, Cp, Cn, and an initial feasible point \alpha
// l is the size of vectors and matrices
// eps is the stopping criterion
//
// solution will be put in \alpha, objective value will be put in obj
//
class Solver
{
internal int active_size;
internal sbyte[] y;
internal double[] G; // gradient of objective function
internal const sbyte LOWER_BOUND = 0;
internal const sbyte UPPER_BOUND = 1;
internal const sbyte FREE = 2;
internal sbyte[] alpha_status; // LOWER_BOUND, UPPER_BOUND, FREE
internal double[] alpha;
internal Kernel Q;
internal double eps;
internal double Cp, Cn;
internal double[] b;
internal int[] active_set;
internal double[] G_bar; // gradient, if we treat free variables as 0
internal int l;
internal bool unshrinked; // XXX
//UPGRADE_NOTE: Final was removed from the declaration of 'INF '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
internal static readonly double INF = System.Double.PositiveInfinity;
internal virtual double get_C(int i)
{
return (y[i] > 0)?Cp:Cn;
}
internal virtual void update_alpha_status(int i)
{
if (alpha[i] >= get_C(i))
alpha_status[i] = UPPER_BOUND;
else if (alpha[i] <= 0)
alpha_status[i] = LOWER_BOUND;
else
alpha_status[i] = FREE;
}
internal virtual bool is_upper_bound(int i)
{
return alpha_status[i] == UPPER_BOUND;
}
internal virtual bool is_lower_bound(int i)
{
return alpha_status[i] == LOWER_BOUND;
}
internal virtual bool is_free(int i)
{
return alpha_status[i] == FREE;
}
// java: information about solution except alpha,
// because we cannot return multiple values otherwise...
internal class SolutionInfo
{
internal double obj;
internal double rho;
internal double upper_bound_p;
internal double upper_bound_n;
internal double r; // for Solver_NU
}
internal virtual void swap_index(int i, int j)
{
Q.swap_index(i, j);
do
{
sbyte _ = y[i]; y[i] = y[j]; y[j] = _;
}
while (false);
do
{
double _ = G[i]; G[i] = G[j]; G[j] = _;
}
while (false);
do
{
sbyte _ = alpha_status[i]; alpha_status[i] = alpha_status[j]; alpha_status[j] = _;
}
while (false);
do
{
double _ = alpha[i]; alpha[i] = alpha[j]; alpha[j] = _;
}
while (false);
do
{
double _ = b[i]; b[i] = b[j]; b[j] = _;
}
while (false);
do
{
int _ = active_set[i]; active_set[i] = active_set[j]; active_set[j] = _;
}
while (false);
do
{
double _ = G_bar[i]; G_bar[i] = G_bar[j]; G_bar[j] = _;
}
while (false);
}
internal virtual void reconstruct_gradient()
{
// reconstruct inactive elements of G from G_bar and free variables
if (active_size == l)
return ;
int i;
for (i = active_size; i < l; i++)
G[i] = G_bar[i] + b[i];
for (i = 0; i < active_size; i++)
if (is_free(i))
{
float[] Q_i = Q.get_Q(i, l);
double alpha_i = alpha[i];
for (int j = active_size; j < l; j++)
G[j] += alpha_i * Q_i[j];
}
}
internal virtual void Solve(int l, Kernel Q, double[] b_, sbyte[] y_, double[] alpha_, double Cp, double Cn, double eps, SolutionInfo si, int shrinking)
{
this.l = l;
this.Q = Q;
b = new double[b_.Length];
b_.CopyTo(b, 0);
y = new sbyte[y_.Length];
y_.CopyTo(y, 0);
alpha = new double[alpha_.Length];
alpha_.CopyTo(alpha, 0);
this.Cp = Cp;
this.Cn = Cn;
this.eps = eps;
this.unshrinked = false;
// initialize alpha_status
{
alpha_status = new sbyte[l];
for (int i = 0; i < l; i++)
update_alpha_status(i);
}
// initialize active set (for shrinking)
{
active_set = new int[l];
for (int i = 0; i < l; i++)
active_set[i] = i;
active_size = l;
}
// initialize gradient
{
G = new double[l];
G_bar = new double[l];
int i;
for (i = 0; i < l; i++)
{
G[i] = b[i];
G_bar[i] = 0;
}
for (i = 0; i < l; i++)
if (!is_lower_bound(i))
{
float[] Q_i = Q.get_Q(i, l);
double alpha_i = alpha[i];
int j;
for (j = 0; j < l; j++)
G[j] += alpha_i * Q_i[j];
if (is_upper_bound(i))
for (j = 0; j < l; j++)
G_bar[j] += get_C(i) * Q_i[j];
}
}
// optimization step
int iter = 0;
int counter = System.Math.Min(l, 1000) + 1;
int[] working_set = new int[2];
while (true)
{
// show progress and do shrinking
if (--counter == 0)
{
counter = System.Math.Min(l, 1000);
if (shrinking != 0)
do_shrinking();
System.Console.Error.Write(".");
}
if (select_working_set(working_set) != 0)
{
// reconstruct the whole gradient
reconstruct_gradient();
// reset active set size and check
active_size = l;
System.Console.Error.Write("*");
if (select_working_set(working_set) != 0)
break;
else
counter = 1; // do shrinking next iteration
}
int i = working_set[0];
int j = working_set[1];
++iter;
// update alpha[i] and alpha[j], handle bounds carefully
float[] Q_i = Q.get_Q(i, active_size);
float[] Q_j = Q.get_Q(j, active_size);
double C_i = get_C(i);
double C_j = get_C(j);
double old_alpha_i = alpha[i];
double old_alpha_j = alpha[j];
if (y[i] != y[j])
{
double delta = (- G[i] - G[j]) / System.Math.Max(Q_i[i] + Q_j[j] + 2 * Q_i[j], (float) 0);
double diff = alpha[i] - alpha[j];
alpha[i] += delta;
alpha[j] += delta;
if (diff > 0)
{
if (alpha[j] < 0)
{
alpha[j] = 0;
alpha[i] = diff;
}
}
else
{
if (alpha[i] < 0)
{
alpha[i] = 0;
alpha[j] = - diff;
}
}
if (diff > C_i - C_j)
{
if (alpha[i] > C_i)
{
alpha[i] = C_i;
alpha[j] = C_i - diff;
}
}
else
{
if (alpha[j] > C_j)
{
alpha[j] = C_j;
alpha[i] = C_j + diff;
}
}
}
else
{
double delta = (G[i] - G[j]) / System.Math.Max(Q_i[i] + Q_j[j] - 2 * Q_i[j], (float) 0);
double sum = alpha[i] + alpha[j];
alpha[i] -= delta;
alpha[j] += delta;
if (sum > C_i)
{
if (alpha[i] > C_i)
{
alpha[i] = C_i;
alpha[j] = sum - C_i;
}
}
else
{
if (alpha[j] < 0)
{
alpha[j] = 0;
alpha[i] = sum;
}
}
if (sum > C_j)
{
if (alpha[j] > C_j)
{
alpha[j] = C_j;
alpha[i] = sum - C_j;
}
}
else
{
if (alpha[i] < 0)
{
alpha[i] = 0;
alpha[j] = sum;
}
}
}
// update G
double delta_alpha_i = alpha[i] - old_alpha_i;
double delta_alpha_j = alpha[j] - old_alpha_j;
for (int k = 0; k < active_size; k++)
{
G[k] += Q_i[k] * delta_alpha_i + Q_j[k] * delta_alpha_j;
}
// update alpha_status and G_bar
{
bool ui = is_upper_bound(i);
bool uj = is_upper_bound(j);
update_alpha_status(i);
update_alpha_status(j);
int k;
if (ui != is_upper_bound(i))
{
Q_i = Q.get_Q(i, l);
if (ui)
for (k = 0; k < l; k++)
G_bar[k] -= C_i * Q_i[k];
else
for (k = 0; k < l; k++)
G_bar[k] += C_i * Q_i[k];
}
if (uj != is_upper_bound(j))
{
Q_j = Q.get_Q(j, l);
if (uj)
for (k = 0; k < l; k++)
G_bar[k] -= C_j * Q_j[k];
else
for (k = 0; k < l; k++)
G_bar[k] += C_j * Q_j[k];
}
}
}
// calculate rho
si.rho = calculate_rho();
// calculate objective value
{
double v = 0;
int i;
for (i = 0; i < l; i++)
v += alpha[i] * (G[i] + b[i]);
si.obj = v / 2;
}
// put back the solution
{
for (int i = 0; i < l; i++)
alpha_[active_set[i]] = alpha[i];
}
si.upper_bound_p = Cp;
si.upper_bound_n = Cn;
System.Console.Out.Write("\noptimization finished, #iter = " + iter + "\n");
}
// return 1 if already optimal, return 0 otherwise
internal virtual int select_working_set(int[] working_set)
{
// return i,j which maximize -grad(f)^T d , under constraint
// if alpha_i == C, d != +1
// if alpha_i == 0, d != -1
double Gmax1 = - INF; // max { -grad(f)_i * d | y_i*d = +1 }
int Gmax1_idx = - 1;
double Gmax2 = - INF; // max { -grad(f)_i * d | y_i*d = -1 }
int Gmax2_idx = - 1;
for (int i = 0; i < active_size; i++)
{
if (y[i] == + 1)
// y = +1
{
if (!is_upper_bound(i))
// d = +1
{
if (- G[i] > Gmax1)
{
Gmax1 = - G[i];
Gmax1_idx = i;
}
}
if (!is_lower_bound(i))
// d = -1
{
if (G[i] > Gmax2)
{
Gmax2 = G[i];
Gmax2_idx = i;
}
}
}
// y = -1
else
{
if (!is_upper_bound(i))
// d = +1
{
if (- G[i] > Gmax2)
{
Gmax2 = - G[i];
Gmax2_idx = i;
}
}
if (!is_lower_bound(i))
// d = -1
{
if (G[i] > Gmax1)
{
Gmax1 = G[i];
Gmax1_idx = i;
}
}
}
}
if (Gmax1 + Gmax2 < eps)
return 1;
working_set[0] = Gmax1_idx;
working_set[1] = Gmax2_idx;
return 0;
}
internal virtual void do_shrinking()
{
int i, j, k;
int[] working_set = new int[2];
if (select_working_set(working_set) != 0)
return ;
i = working_set[0];
j = working_set[1];
double Gm1 = (- y[j]) * G[j];
double Gm2 = y[i] * G[i];
// shrink
for (k = 0; k < active_size; k++)
{
if (is_lower_bound(k))
{
if (y[k] == + 1)
{
if (- G[k] >= Gm1)
continue;
}
else if (- G[k] >= Gm2)
continue;
}
else if (is_upper_bound(k))
{
if (y[k] == + 1)
{
if (G[k] >= Gm2)
continue;
}
else if (G[k] >= Gm1)
continue;
}
else
continue;
--active_size;
swap_index(k, active_size);
--k; // look at the newcomer
}
// unshrink, check all variables again before final iterations
if (unshrinked || - (Gm1 + Gm2) > eps * 10)
return ;
unshrinked = true;
reconstruct_gradient();
for (k = l - 1; k >= active_size; k--)
{
if (is_lower_bound(k))
{
if (y[k] == + 1)
{
if (- G[k] < Gm1)
continue;
}
else if (- G[k] < Gm2)
continue;
}
else if (is_upper_bound(k))
{
if (y[k] == + 1)
{
if (G[k] < Gm2)
continue;
}
else if (G[k] < Gm1)
continue;
}
else
continue;
swap_index(k, active_size);
active_size++;
++k; // look at the newcomer
}
}
internal virtual double calculate_rho()
{
double r;
int nr_free = 0;
double ub = INF, lb = - INF, sum_free = 0;
for (int i = 0; i < active_size; i++)
{
double yG = y[i] * G[i];
if (is_lower_bound(i))
{
if (y[i] > 0)
ub = System.Math.Min(ub, yG);
else
lb = System.Math.Max(lb, yG);
}
else if (is_upper_bound(i))
{
if (y[i] < 0)
ub = System.Math.Min(ub, yG);
else
lb = System.Math.Max(lb, yG);
}
else
{
++nr_free;
sum_free += yG;
}
}
if (nr_free > 0)
r = sum_free / nr_free;
else
r = (ub + lb) / 2;
return r;
}
}
//
// Solver for nu-svm classification and regression
//
// additional constraint: e^T \alpha = constant
//
sealed class Solver_NU:Solver
{
private SolutionInfo si;
internal override void Solve(int l, Kernel Q, double[] b, sbyte[] y, double[] alpha, double Cp, double Cn, double eps, SolutionInfo si, int shrinking)
{
this.si = si;
base.Solve(l, Q, b, y, alpha, Cp, Cn, eps, si, shrinking);
}
internal override int select_working_set(int[] working_set)
{
// return i,j which maximize -grad(f)^T d , under constraint
// if alpha_i == C, d != +1
// if alpha_i == 0, d != -1
double Gmax1 = - INF; // max { -grad(f)_i * d | y_i = +1, d = +1 }
int Gmax1_idx = - 1;
double Gmax2 = - INF; // max { -grad(f)_i * d | y_i = +1, d = -1 }
int Gmax2_idx = - 1;
double Gmax3 = - INF; // max { -grad(f)_i * d | y_i = -1, d = +1 }
int Gmax3_idx = - 1;
double Gmax4 = - INF; // max { -grad(f)_i * d | y_i = -1, d = -1 }
int Gmax4_idx = - 1;
for (int i = 0; i < active_size; i++)
{
if (y[i] == + 1)
// y == +1
{
if (!is_upper_bound(i))
// d = +1
{
if (- G[i] > Gmax1)
{
Gmax1 = - G[i];
Gmax1_idx = i;
}
}
if (!is_lower_bound(i))
// d = -1
{
if (G[i] > Gmax2)
{
Gmax2 = G[i];
Gmax2_idx = i;
}
}
}
// y == -1
else
{
if (!is_upper_bound(i))
// d = +1
{
if (- G[i] > Gmax3)
{
Gmax3 = - G[i];
Gmax3_idx = i;
}
}
if (!is_lower_bound(i))
// d = -1
{
if (G[i] > Gmax4)
{
Gmax4 = G[i];
Gmax4_idx = i;
}
}
}
}
if (System.Math.Max(Gmax1 + Gmax2, Gmax3 + Gmax4) < eps)
return 1;
if (Gmax1 + Gmax2 > Gmax3 + Gmax4)
{
working_set[0] = Gmax1_idx;
working_set[1] = Gmax2_idx;
}
else
{
working_set[0] = Gmax3_idx;
working_set[1] = Gmax4_idx;
}
return 0;
}
internal override void do_shrinking()
{
double Gmax1 = - INF; // max { -grad(f)_i * d | y_i = +1, d = +1 }
double Gmax2 = - INF; // max { -grad(f)_i * d | y_i = +1, d = -1 }
double Gmax3 = - INF; // max { -grad(f)_i * d | y_i = -1, d = +1 }
double Gmax4 = - INF; // max { -grad(f)_i * d | y_i = -1, d = -1 }
int k;
for (k = 0; k < active_size; k++)
{
if (!is_upper_bound(k))
{
if (y[k] == + 1)
{
if (- G[k] > Gmax1)
Gmax1 = - G[k];
}
else if (- G[k] > Gmax3)
Gmax3 = - G[k];
}
if (!is_lower_bound(k))
{
if (y[k] == + 1)
{
if (G[k] > Gmax2)
Gmax2 = G[k];
}
else if (G[k] > Gmax4)
Gmax4 = G[k];
}
}
double Gm1 = - Gmax2;
double Gm2 = - Gmax1;
double Gm3 = - Gmax4;
double Gm4 = - Gmax3;
for (k = 0; k < active_size; k++)
{
if (is_lower_bound(k))
{
if (y[k] == + 1)
{
if (- G[k] >= Gm1)
continue;
}
else if (- G[k] >= Gm3)
continue;
}
else if (is_upper_bound(k))
{
if (y[k] == + 1)
{
if (G[k] >= Gm2)
continue;
}
else if (G[k] >= Gm4)
continue;
}
else
continue;
--active_size;
swap_index(k, active_size);
--k; // look at the newcomer
}
// unshrink, check all variables again before final iterations
if (unshrinked || System.Math.Max(- (Gm1 + Gm2), - (Gm3 + Gm4)) > eps * 10)
return ;
unshrinked = true;
reconstruct_gradient();
for (k = l - 1; k >= active_size; k--)
{
if (is_lower_bound(k))
{
if (y[k] == + 1)
{
if (- G[k] < Gm1)
continue;
}
else if (- G[k] < Gm3)
continue;
}
else if (is_upper_bound(k))
{
if (y[k] == + 1)
{
if (G[k] < Gm2)
continue;
}
else if (G[k] < Gm4)
continue;
}
else
continue;
swap_index(k, active_size);
active_size++;
++k; // look at the newcomer
}
}
internal override double calculate_rho()
{
int nr_free1 = 0, nr_free2 = 0;
double ub1 = INF, ub2 = INF;
double lb1 = - INF, lb2 = - INF;
double sum_free1 = 0, sum_free2 = 0;
for (int i = 0; i < active_size; i++)
{
if (y[i] == + 1)
{
if (is_lower_bound(i))
ub1 = System.Math.Min(ub1, G[i]);
else if (is_upper_bound(i))
lb1 = System.Math.Max(lb1, G[i]);
else
{
++nr_free1;
sum_free1 += G[i];
}
}
else
{
if (is_lower_bound(i))
ub2 = System.Math.Min(ub2, G[i]);
else if (is_upper_bound(i))
lb2 = System.Math.Max(lb2, G[i]);
else
{
++nr_free2;
sum_free2 += G[i];
}
}
}
double r1, r2;
if (nr_free1 > 0)
r1 = sum_free1 / nr_free1;
else
r1 = (ub1 + lb1) / 2;
if (nr_free2 > 0)
r2 = sum_free2 / nr_free2;
else
r2 = (ub2 + lb2) / 2;
si.r = (r1 + r2) / 2;
return (r1 - r2) / 2;
}
}
//
// Q matrices for various formulations
//
class SVC_Q:Kernel
{
//UPGRADE_NOTE: Final was removed from the declaration of 'y '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private sbyte[] y;
//UPGRADE_NOTE: Final was removed from the declaration of 'cache '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private Cache cache;
internal SVC_Q(svm_problem prob, svm_parameter param, sbyte[] y_):base(prob.l, prob.x, param)
{
y = new sbyte[y_.Length];
y_.CopyTo(y, 0);
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
cache = new Cache(prob.l, (int) (param.cache_size * (1 << 20)));
}
internal override float[] get_Q(int i, int len)
{
float[][] data = new float[1][];
int start;
if ((start = cache.get_data(i, data, len)) < len)
{
for (int j = start; j < len; j++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
data[0][j] = (float) (y[i] * y[j] * kernel_function(i, j));
}
}
return data[0];
}
internal override void swap_index(int i, int j)
{
cache.swap_index(i, j);
base.swap_index(i, j);
do
{
sbyte _ = y[i]; y[i] = y[j]; y[j] = _;
}
while (false);
}
}
class ONE_CLASS_Q:Kernel
{
//UPGRADE_NOTE: Final was removed from the declaration of 'cache '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private Cache cache;
internal ONE_CLASS_Q(svm_problem prob, svm_parameter param):base(prob.l, prob.x, param)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
cache = new Cache(prob.l, (int) (param.cache_size * (1 << 20)));
}
internal override float[] get_Q(int i, int len)
{
float[][] data = new float[1][];
int start;
if ((start = cache.get_data(i, data, len)) < len)
{
for (int j = start; j < len; j++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
data[0][j] = (float) kernel_function(i, j);
}
}
return data[0];
}
internal override void swap_index(int i, int j)
{
cache.swap_index(i, j);
base.swap_index(i, j);
}
}
class SVR_Q:Kernel
{
//UPGRADE_NOTE: Final was removed from the declaration of 'l '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private int l;
//UPGRADE_NOTE: Final was removed from the declaration of 'cache '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private Cache cache;
//UPGRADE_NOTE: Final was removed from the declaration of 'sign '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private sbyte[] sign;
//UPGRADE_NOTE: Final was removed from the declaration of 'index '. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
private int[] index;
private int next_buffer;
private float[][] buffer;
internal SVR_Q(svm_problem prob, svm_parameter param):base(prob.l, prob.x, param)
{
l = prob.l;
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
cache = new Cache(l, (int) (param.cache_size * (1 << 20)));
sign = new sbyte[2 * l];
index = new int[2 * l];
for (int k = 0; k < l; k++)
{
sign[k] = 1;
sign[k + l] = - 1;
index[k] = k;
index[k + l] = k;
}
buffer = new float[2][];
for (int i = 0; i < 2; i++)
{
buffer[i] = new float[2 * l];
}
next_buffer = 0;
}
internal override void swap_index(int i, int j)
{
do
{
sbyte _ = sign[i]; sign[i] = sign[j]; sign[j] = _;
}
while (false);
do
{
int _ = index[i]; index[i] = index[j]; index[j] = _;
}
while (false);
}
internal override float[] get_Q(int i, int len)
{
float[][] data = new float[1][];
int real_i = index[i];
if (cache.get_data(real_i, data, l) < l)
{
for (int j = 0; j < l; j++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
data[0][j] = (float) kernel_function(real_i, j);
}
}
// reorder and copy
float[] buf = buffer[next_buffer];
next_buffer = 1 - next_buffer;
sbyte si = sign[i];
for (int j = 0; j < len; j++)
buf[j] = si * sign[j] * data[0][index[j]];
return buf;
}
}
public class svm
{
//
// construct and solve various formulations
//
private static void solve_c_svc(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si, double Cp, double Cn)
{
int l = prob.l;
double[] minus_ones = new double[l];
sbyte[] y = new sbyte[l];
int i;
for (i = 0; i < l; i++)
{
alpha[i] = 0;
minus_ones[i] = - 1;
if (prob.y[i] > 0)
y[i] = (sbyte) (+ 1);
else
y[i] = - 1;
}
Solver s = new Solver();
s.Solve(l, new SVC_Q(prob, param, y), minus_ones, y, alpha, Cp, Cn, param.eps, si, param.shrinking);
double sum_alpha = 0;
for (i = 0; i < l; i++)
sum_alpha += alpha[i];
if (Cp == Cn)
System.Console.Out.Write("nu = " + sum_alpha / (Cp * prob.l) + "\n");
for (i = 0; i < l; i++)
alpha[i] *= y[i];
}
private static void solve_nu_svc(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si)
{
int i;
int l = prob.l;
double nu = param.nu;
sbyte[] y = new sbyte[l];
for (i = 0; i < l; i++)
if (prob.y[i] > 0)
y[i] = (sbyte) (+ 1);
else
y[i] = - 1;
double sum_pos = nu * l / 2;
double sum_neg = nu * l / 2;
for (i = 0; i < l; i++)
if (y[i] == + 1)
{
alpha[i] = System.Math.Min(1.0, sum_pos);
sum_pos -= alpha[i];
}
else
{
alpha[i] = System.Math.Min(1.0, sum_neg);
sum_neg -= alpha[i];
}
double[] zeros = new double[l];
for (i = 0; i < l; i++)
zeros[i] = 0;
Solver_NU s = new Solver_NU();
s.Solve(l, new SVC_Q(prob, param, y), zeros, y, alpha, 1.0, 1.0, param.eps, si, param.shrinking);
double r = si.r;
System.Console.Out.Write("C = " + 1 / r + "\n");
for (i = 0; i < l; i++)
alpha[i] *= y[i] / r;
si.rho /= r;
si.obj /= (r * r);
si.upper_bound_p = 1 / r;
si.upper_bound_n = 1 / r;
}
private static void solve_one_class(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si)
{
int l = prob.l;
double[] zeros = new double[l];
sbyte[] ones = new sbyte[l];
int i;
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int n = (int) (param.nu * prob.l); // # of alpha's at upper bound
for (i = 0; i < n; i++)
alpha[i] = 1;
alpha[n] = param.nu * prob.l - n;
for (i = n + 1; i < l; i++)
alpha[i] = 0;
for (i = 0; i < l; i++)
{
zeros[i] = 0;
ones[i] = 1;
}
Solver s = new Solver();
s.Solve(l, new ONE_CLASS_Q(prob, param), zeros, ones, alpha, 1.0, 1.0, param.eps, si, param.shrinking);
}
private static void solve_epsilon_svr(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si)
{
int l = prob.l;
double[] alpha2 = new double[2 * l];
double[] linear_term = new double[2 * l];
sbyte[] y = new sbyte[2 * l];
int i;
for (i = 0; i < l; i++)
{
alpha2[i] = 0;
linear_term[i] = param.p - prob.y[i];
y[i] = 1;
alpha2[i + l] = 0;
linear_term[i + l] = param.p + prob.y[i];
y[i + l] = - 1;
}
Solver s = new Solver();
s.Solve(2 * l, new SVR_Q(prob, param), linear_term, y, alpha2, param.C, param.C, param.eps, si, param.shrinking);
double sum_alpha = 0;
for (i = 0; i < l; i++)
{
alpha[i] = alpha2[i] - alpha2[i + l];
sum_alpha += System.Math.Abs(alpha[i]);
}
System.Console.Out.Write("nu = " + sum_alpha / (param.C * l) + "\n");
}
private static void solve_nu_svr(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si)
{
int l = prob.l;
double C = param.C;
double[] alpha2 = new double[2 * l];
double[] linear_term = new double[2 * l];
sbyte[] y = new sbyte[2 * l];
int i;
double sum = C * param.nu * l / 2;
for (i = 0; i < l; i++)
{
alpha2[i] = alpha2[i + l] = System.Math.Min(sum, C);
sum -= alpha2[i];
linear_term[i] = - prob.y[i];
y[i] = 1;
linear_term[i + l] = prob.y[i];
y[i + l] = - 1;
}
Solver_NU s = new Solver_NU();
s.Solve(2 * l, new SVR_Q(prob, param), linear_term, y, alpha2, C, C, param.eps, si, param.shrinking);
System.Console.Out.Write("epsilon = " + (- si.r) + "\n");
for (i = 0; i < l; i++)
alpha[i] = alpha2[i] - alpha2[i + l];
}
//
// decision_function
//
internal class decision_function
{
internal double[] alpha;
internal double rho;
}
internal static decision_function svm_train_one(svm_problem prob, svm_parameter param, double Cp, double Cn)
{
double[] alpha = new double[prob.l];
Solver.SolutionInfo si = new Solver.SolutionInfo();
switch (param.svm_type)
{
case svm_parameter.C_SVC:
solve_c_svc(prob, param, alpha, si, Cp, Cn);
break;
case svm_parameter.NU_SVC:
solve_nu_svc(prob, param, alpha, si);
break;
case svm_parameter.ONE_CLASS:
solve_one_class(prob, param, alpha, si);
break;
case svm_parameter.EPSILON_SVR:
solve_epsilon_svr(prob, param, alpha, si);
break;
case svm_parameter.NU_SVR:
solve_nu_svr(prob, param, alpha, si);
break;
}
System.Console.Out.Write("obj = " + si.obj + ", rho = " + si.rho + "\n");
// output SVs
int nSV = 0;
int nBSV = 0;
for (int i = 0; i < prob.l; i++)
{
if (System.Math.Abs(alpha[i]) > 0)
{
++nSV;
if (prob.y[i] > 0)
{
if (System.Math.Abs(alpha[i]) >= si.upper_bound_p)
++nBSV;
}
else
{
if (System.Math.Abs(alpha[i]) >= si.upper_bound_n)
++nBSV;
}
}
}
System.Console.Out.Write("nSV = " + nSV + ", nBSV = " + nBSV + "\n");
decision_function f = new decision_function();
f.alpha = alpha;
f.rho = si.rho;
return f;
}
// Platt's binary SVM Probablistic Output: an improvement from Lin et al.
private static void sigmoid_train(int l, double[] dec_values, double[] labels, double[] probAB)
{
double A, B;
double prior1 = 0, prior0 = 0;
int i;
for (i = 0; i < l; i++)
if (labels[i] > 0)
prior1 += 1;
else
prior0 += 1;
int max_iter = 100; // Maximal number of iterations
double min_step = 1e-10; // Minimal step taken in line search
double sigma = 1e-3; // For numerically strict PD of Hessian
double eps = 1e-5;
double hiTarget = (prior1 + 1.0) / (prior1 + 2.0);
double loTarget = 1 / (prior0 + 2.0);
double[] t = new double[l];
double fApB, p, q, h11, h22, h21, g1, g2, det, dA, dB, gd, stepsize;
double newA, newB, newf, d1, d2;
int iter;
// Initial Point and Initial Fun Value
A = 0.0; B = System.Math.Log((prior0 + 1.0) / (prior1 + 1.0));
double fval = 0.0;
for (i = 0; i < l; i++)
{
if (labels[i] > 0)
t[i] = hiTarget;
else
t[i] = loTarget;
fApB = dec_values[i] * A + B;
if (fApB >= 0)
fval += t[i] * fApB + System.Math.Log(1 + System.Math.Exp(- fApB));
else
fval += (t[i] - 1) * fApB + System.Math.Log(1 + System.Math.Exp(fApB));
}
for (iter = 0; iter < max_iter; iter++)
{
// Update Gradient and Hessian (use H' = H + sigma I)
h11 = sigma; // numerically ensures strict PD
h22 = sigma;
h21 = 0.0; g1 = 0.0; g2 = 0.0;
for (i = 0; i < l; i++)
{
fApB = dec_values[i] * A + B;
if (fApB >= 0)
{
p = System.Math.Exp(- fApB) / (1.0 + System.Math.Exp(- fApB));
q = 1.0 / (1.0 + System.Math.Exp(- fApB));
}
else
{
p = 1.0 / (1.0 + System.Math.Exp(fApB));
q = System.Math.Exp(fApB) / (1.0 + System.Math.Exp(fApB));
}
d2 = p * q;
h11 += dec_values[i] * dec_values[i] * d2;
h22 += d2;
h21 += dec_values[i] * d2;
d1 = t[i] - p;
g1 += dec_values[i] * d1;
g2 += d1;
}
// Stopping Criteria
if (System.Math.Abs(g1) < eps && System.Math.Abs(g2) < eps)
break;
// Finding Newton direction: -inv(H') * g
det = h11 * h22 - h21 * h21;
dA = (- (h22 * g1 - h21 * g2)) / det;
dB = (- ((- h21) * g1 + h11 * g2)) / det;
gd = g1 * dA + g2 * dB;
stepsize = 1; // Line Search
while (stepsize >= min_step)
{
newA = A + stepsize * dA;
newB = B + stepsize * dB;
// New function value
newf = 0.0;
for (i = 0; i < l; i++)
{
fApB = dec_values[i] * newA + newB;
if (fApB >= 0)
newf += t[i] * fApB + System.Math.Log(1 + System.Math.Exp(- fApB));
else
newf += (t[i] - 1) * fApB + System.Math.Log(1 + System.Math.Exp(fApB));
}
// Check sufficient decrease
if (newf < fval + 0.0001 * stepsize * gd)
{
A = newA; B = newB; fval = newf;
break;
}
else
stepsize = stepsize / 2.0;
}
if (stepsize < min_step)
{
System.Console.Error.Write("Line search fails in two-class probability estimates\n");
break;
}
}
if (iter >= max_iter)
System.Console.Error.Write("Reaching maximal iterations in two-class probability estimates\n");
probAB[0] = A; probAB[1] = B;
}
private static double sigmoid_predict(double decision_value, double A, double B)
{
double fApB = decision_value * A + B;
if (fApB >= 0)
return System.Math.Exp(- fApB) / (1.0 + System.Math.Exp(- fApB));
else
return 1.0 / (1 + System.Math.Exp(fApB));
}
// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
private static void multiclass_probability(int k, double[][] r, double[] p)
{
int t;
int iter = 0, max_iter = 100;
double[][] Q = new double[k][];
for (int i = 0; i < k; i++)
{
Q[i] = new double[k];
}
double[] Qp = new double[k];
double pQp, eps = 0.001;
for (t = 0; t < k; t++)
{
p[t] = 1.0 / k; // Valid if k = 1
Q[t][t] = 0;
for (int j = 0; j < t; j++)
{
Q[t][t] += r[j][t] * r[j][t];
Q[t][j] = Q[j][t];
}
for (int j = t + 1; j < k; j++)
{
Q[t][t] += r[j][t] * r[j][t];
Q[t][j] = (- r[j][t]) * r[t][j];
}
}
for (iter = 0; iter < max_iter; iter++)
{
// stopping condition, recalculate QP,pQP for numerical accuracy
pQp = 0;
for (t = 0; t < k; t++)
{
Qp[t] = 0;
for (int j = 0; j < k; j++)
Qp[t] += Q[t][j] * p[j];
pQp += p[t] * Qp[t];
}
double max_error = 0;
for (t = 0; t < k; t++)
{
double error = System.Math.Abs(Qp[t] - pQp);
if (error > max_error)
max_error = error;
}
if (max_error < eps)
break;
for (t = 0; t < k; t++)
{
double diff = (- Qp[t] + pQp) / Q[t][t];
p[t] += diff;
pQp = (pQp + diff * (diff * Q[t][t] + 2 * Qp[t])) / (1 + diff) / (1 + diff);
for (int j = 0; j < k; j++)
{
Qp[j] = (Qp[j] + diff * Q[t][j]) / (1 + diff);
p[j] /= (1 + diff);
}
}
}
if (iter >= max_iter)
System.Console.Error.Write("Exceeds max_iter in multiclass_prob\n");
}
// Cross-validation decision values for probability estimates
private static void svm_binary_svc_probability(svm_problem prob, svm_parameter param, double Cp, double Cn, double[] probAB)
{
int i;
int nr_fold = 5;
int[] perm = new int[prob.l];
double[] dec_values = new double[prob.l];
// random shuffle
for (i = 0; i < prob.l; i++)
perm[i] = i;
for (i = 0; i < prob.l; i++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int j = i + (int) (SupportClass.Random.NextDouble() * (prob.l - i));
do
{
int _ = perm[i]; perm[i] = perm[j]; perm[j] = _;
}
while (false);
}
for (i = 0; i < nr_fold; i++)
{
int begin = i * prob.l / nr_fold;
int end = (i + 1) * prob.l / nr_fold;
int j, k;
svm_problem subprob = new svm_problem();
subprob.l = prob.l - (end - begin);
subprob.x = new svm_node[subprob.l][];
subprob.y = new double[subprob.l];
k = 0;
for (j = 0; j < begin; j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
for (j = end; j < prob.l; j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
int p_count = 0, n_count = 0;
for (j = 0; j < k; j++)
if (subprob.y[j] > 0)
p_count++;
else
n_count++;
if (p_count == 0 && n_count == 0)
for (j = begin; j < end; j++)
dec_values[perm[j]] = 0;
else if (p_count > 0 && n_count == 0)
for (j = begin; j < end; j++)
dec_values[perm[j]] = 1;
else if (p_count == 0 && n_count > 0)
for (j = begin; j < end; j++)
dec_values[perm[j]] = - 1;
else
{
svm_parameter subparam = (svm_parameter) param.Clone();
subparam.probability = 0;
subparam.C = 1.0;
subparam.nr_weight = 2;
subparam.weight_label = new int[2];
subparam.weight = new double[2];
subparam.weight_label[0] = + 1;
subparam.weight_label[1] = - 1;
subparam.weight[0] = Cp;
subparam.weight[1] = Cn;
svm_model submodel = svm_train(subprob, subparam);
for (j = begin; j < end; j++)
{
double[] dec_value = new double[1];
svm_predict_values(submodel, prob.x[perm[j]], dec_value);
dec_values[perm[j]] = dec_value[0];
// ensure +1 -1 order; reason not using CV subroutine
dec_values[perm[j]] *= submodel.label[0];
}
}
}
sigmoid_train(prob.l, dec_values, prob.y, probAB);
}
// Return parameter of a Laplace distribution
private static double svm_svr_probability(svm_problem prob, svm_parameter param)
{
int i;
int nr_fold = 5;
double[] ymv = new double[prob.l];
double mae = 0;
svm_parameter newparam = (svm_parameter) param.Clone();
newparam.probability = 0;
svm_cross_validation(prob, newparam, nr_fold, ymv);
for (i = 0; i < prob.l; i++)
{
ymv[i] = prob.y[i] - ymv[i];
mae += System.Math.Abs(ymv[i]);
}
mae /= prob.l;
double std = System.Math.Sqrt(2 * mae * mae);
int count = 0;
mae = 0;
for (i = 0; i < prob.l; i++)
if (System.Math.Abs(ymv[i]) > 5 * std)
count = count + 1;
else
mae += System.Math.Abs(ymv[i]);
mae /= (prob.l - count);
System.Console.Error.Write("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + mae + "\n");
return mae;
}
//
// Interface functions
//
public static svm_model svm_train(svm_problem prob, svm_parameter param)
{
svm_model model = new svm_model();
model.param = param;
if (param.svm_type == svm_parameter.ONE_CLASS || param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR)
{
// regression or one-class-svm
model.nr_class = 2;
model.label = null;
model.nSV = null;
model.probA = null; model.probB = null;
model.sv_coef = new double[1][];
if (param.probability == 1 && (param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR))
{
model.probA = new double[1];
model.probA[0] = svm_svr_probability(prob, param);
}
decision_function f = svm_train_one(prob, param, 0, 0);
model.rho = new double[1];
model.rho[0] = f.rho;
int nSV = 0;
int i;
for (i = 0; i < prob.l; i++)
if (System.Math.Abs(f.alpha[i]) > 0)
++nSV;
model.l = nSV;
model.SV = new svm_node[nSV][];
model.sv_coef[0] = new double[nSV];
int j = 0;
for (i = 0; i < prob.l; i++)
if (System.Math.Abs(f.alpha[i]) > 0)
{
model.SV[j] = prob.x[i];
model.sv_coef[0][j] = f.alpha[i];
++j;
}
}
else
{
// classification
// find out the number of classes
int l = prob.l;
int max_nr_class = 16;
int nr_class = 0;
int[] label = new int[max_nr_class];
int[] count = new int[max_nr_class];
int[] index = new int[l];
int i;
for (i = 0; i < l; i++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int this_label = (int) prob.y[i];
int j;
for (j = 0; j < nr_class; j++)
if (this_label == label[j])
{
++count[j];
break;
}
index[i] = j;
if (j == nr_class)
{
if (nr_class == max_nr_class)
{
max_nr_class *= 2;
int[] new_data = new int[max_nr_class];
Array.Copy(label, 0, new_data, 0, label.Length);
label = new_data;
new_data = new int[max_nr_class];
Array.Copy(count, 0, new_data, 0, count.Length);
count = new_data;
}
label[nr_class] = this_label;
count[nr_class] = 1;
++nr_class;
}
}
// group training data of the same class
int[] start = new int[nr_class];
start[0] = 0;
for (i = 1; i < nr_class; i++)
start[i] = start[i - 1] + count[i - 1];
svm_node[][] x = new svm_node[l][];
for (i = 0; i < l; i++)
{
x[start[index[i]]] = prob.x[i];
++start[index[i]];
}
start[0] = 0;
for (i = 1; i < nr_class; i++)
start[i] = start[i - 1] + count[i - 1];
// calculate weighted C
double[] weighted_C = new double[nr_class];
for (i = 0; i < nr_class; i++)
weighted_C[i] = param.C;
for (i = 0; i < param.nr_weight; i++)
{
int j;
for (j = 0; j < nr_class; j++)
if (param.weight_label[i] == label[j])
break;
if (j == nr_class)
System.Console.Error.Write("warning: class label " + param.weight_label[i] + " specified in weight is not found\n");
else
weighted_C[j] *= param.weight[i];
}
// train k*(k-1)/2 models
bool[] nonzero = new bool[l];
for (i = 0; i < l; i++)
nonzero[i] = false;
decision_function[] f = new decision_function[nr_class * (nr_class - 1) / 2];
double[] probA = null, probB = null;
if (param.probability == 1)
{
probA = new double[nr_class * (nr_class - 1) / 2];
probB = new double[nr_class * (nr_class - 1) / 2];
}
int p = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
svm_problem sub_prob = new svm_problem();
int si = start[i], sj = start[j];
int ci = count[i], cj = count[j];
sub_prob.l = ci + cj;
sub_prob.x = new svm_node[sub_prob.l][];
sub_prob.y = new double[sub_prob.l];
int k;
for (k = 0; k < ci; k++)
{
sub_prob.x[k] = x[si + k];
sub_prob.y[k] = + 1;
}
for (k = 0; k < cj; k++)
{
sub_prob.x[ci + k] = x[sj + k];
sub_prob.y[ci + k] = - 1;
}
if (param.probability == 1)
{
double[] probAB = new double[2];
svm_binary_svc_probability(sub_prob, param, weighted_C[i], weighted_C[j], probAB);
probA[p] = probAB[0];
probB[p] = probAB[1];
}
f[p] = svm_train_one(sub_prob, param, weighted_C[i], weighted_C[j]);
for (k = 0; k < ci; k++)
if (!nonzero[si + k] && System.Math.Abs(f[p].alpha[k]) > 0)
nonzero[si + k] = true;
for (k = 0; k < cj; k++)
if (!nonzero[sj + k] && System.Math.Abs(f[p].alpha[ci + k]) > 0)
nonzero[sj + k] = true;
++p;
}
// build output
model.nr_class = nr_class;
model.label = new int[nr_class];
for (i = 0; i < nr_class; i++)
model.label[i] = label[i];
model.rho = new double[nr_class * (nr_class - 1) / 2];
for (i = 0; i < nr_class * (nr_class - 1) / 2; i++)
model.rho[i] = f[i].rho;
if (param.probability == 1)
{
model.probA = new double[nr_class * (nr_class - 1) / 2];
model.probB = new double[nr_class * (nr_class - 1) / 2];
for (i = 0; i < nr_class * (nr_class - 1) / 2; i++)
{
model.probA[i] = probA[i];
model.probB[i] = probB[i];
}
}
else
{
model.probA = null;
model.probB = null;
}
int nnz = 0;
int[] nz_count = new int[nr_class];
model.nSV = new int[nr_class];
for (i = 0; i < nr_class; i++)
{
int nSV = 0;
for (int j = 0; j < count[i]; j++)
if (nonzero[start[i] + j])
{
++nSV;
++nnz;
}
model.nSV[i] = nSV;
nz_count[i] = nSV;
}
System.Console.Out.Write("Total nSV = " + nnz + "\n");
model.l = nnz;
model.SV = new svm_node[nnz][];
p = 0;
for (i = 0; i < l; i++)
if (nonzero[i])
model.SV[p++] = x[i];
int[] nz_start = new int[nr_class];
nz_start[0] = 0;
for (i = 1; i < nr_class; i++)
nz_start[i] = nz_start[i - 1] + nz_count[i - 1];
model.sv_coef = new double[nr_class - 1][];
for (i = 0; i < nr_class - 1; i++)
model.sv_coef[i] = new double[nnz];
p = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
// classifier (i,j): coefficients with
// i are in sv_coef[j-1][nz_start[i]...],
// j are in sv_coef[i][nz_start[j]...]
int si = start[i];
int sj = start[j];
int ci = count[i];
int cj = count[j];
int q = nz_start[i];
int k;
for (k = 0; k < ci; k++)
if (nonzero[si + k])
model.sv_coef[j - 1][q++] = f[p].alpha[k];
q = nz_start[j];
for (k = 0; k < cj; k++)
if (nonzero[sj + k])
model.sv_coef[i][q++] = f[p].alpha[ci + k];
++p;
}
}
return model;
}
public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target)
{
int i;
int[] perm = new int[prob.l];
// random shuffle
for (i = 0; i < prob.l; i++)
perm[i] = i;
for (i = 0; i < prob.l; i++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int j = i + (int) (SupportClass.Random.NextDouble() * (prob.l - i));
do
{
int _ = perm[i]; perm[i] = perm[j]; perm[j] = _;
}
while (false);
}
for (i = 0; i < nr_fold; i++)
{
int begin = i * prob.l / nr_fold;
int end = (i + 1) * prob.l / nr_fold;
int j, k;
svm_problem subprob = new svm_problem();
subprob.l = prob.l - (end - begin);
subprob.x = new svm_node[subprob.l][];
subprob.y = new double[subprob.l];
k = 0;
for (j = 0; j < begin; j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
for (j = end; j < prob.l; j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
svm_model submodel = svm_train(subprob, param);
if (param.probability == 1 && (param.svm_type == svm_parameter.C_SVC || param.svm_type == svm_parameter.NU_SVC))
{
double[] prob_estimates = new double[svm_get_nr_class(submodel)];
for (j = begin; j < end; j++)
target[perm[j]] = svm_predict_probability(submodel, prob.x[perm[j]], prob_estimates);
}
else
for (j = begin; j < end; j++)
target[perm[j]] = svm_predict(submodel, prob.x[perm[j]]);
}
}
public static int svm_get_svm_type(svm_model model)
{
return model.param.svm_type;
}
public static int svm_get_nr_class(svm_model model)
{
return model.nr_class;
}
public static void svm_get_labels(svm_model model, int[] label)
{
if (model.label != null)
for (int i = 0; i < model.nr_class; i++)
label[i] = model.label[i];
}
public static double svm_get_svr_probability(svm_model model)
{
if ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) && model.probA != null)
return model.probA[0];
else
{
System.Console.Error.Write("Model doesn't contain information for SVR probability inference\n");
return 0;
}
}
public static void svm_predict_values(svm_model model, svm_node[] x, double[] dec_values)
{
if (model.param.svm_type == svm_parameter.ONE_CLASS || model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR)
{
double[] sv_coef = model.sv_coef[0];
double sum = 0;
for (int i = 0; i < model.l; i++)
sum += sv_coef[i] * Kernel.k_function(x, model.SV[i], model.param);
sum -= model.rho[0];
dec_values[0] = sum;
}
else
{
int i;
int nr_class = model.nr_class;
int l = model.l;
double[] kvalue = new double[l];
for (i = 0; i < l; i++)
kvalue[i] = Kernel.k_function(x, model.SV[i], model.param);
int[] start = new int[nr_class];
start[0] = 0;
for (i = 1; i < nr_class; i++)
start[i] = start[i - 1] + model.nSV[i - 1];
int p = 0;
int pos = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
double sum = 0;
int si = start[i];
int sj = start[j];
int ci = model.nSV[i];
int cj = model.nSV[j];
int k;
double[] coef1 = model.sv_coef[j - 1];
double[] coef2 = model.sv_coef[i];
for (k = 0; k < ci; k++)
sum += coef1[si + k] * kvalue[si + k];
for (k = 0; k < cj; k++)
sum += coef2[sj + k] * kvalue[sj + k];
sum -= model.rho[p++];
dec_values[pos++] = sum;
}
}
}
public static double svm_predict(svm_model model, svm_node[] x)
{
if (model.param.svm_type == svm_parameter.ONE_CLASS || model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR)
{
double[] res = new double[1];
svm_predict_values(model, x, res);
if (model.param.svm_type == svm_parameter.ONE_CLASS)
return (res[0] > 0)?1:- 1;
else
return res[0];
}
else
{
int i;
int nr_class = model.nr_class;
double[] dec_values = new double[nr_class * (nr_class - 1) / 2];
svm_predict_values(model, x, dec_values);
int[] vote = new int[nr_class];
for (i = 0; i < nr_class; i++)
vote[i] = 0;
int pos = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
if (dec_values[pos++] > 0)
++vote[i];
else
++vote[j];
}
int vote_max_idx = 0;
for (i = 1; i < nr_class; i++)
if (vote[i] > vote[vote_max_idx])
vote_max_idx = i;
return model.label[vote_max_idx];
}
}
public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates)
{
if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) && model.probA != null && model.probB != null)
{
int i;
int nr_class = model.nr_class;
double[] dec_values = new double[nr_class * (nr_class - 1) / 2];
svm_predict_values(model, x, dec_values);
double min_prob = 1e-7;
double[][] tmpArray = new double[nr_class][];
for (int i2 = 0; i2 < nr_class; i2++)
{
tmpArray[i2] = new double[nr_class];
}
double[][] pairwise_prob = tmpArray;
int k = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
pairwise_prob[i][j] = System.Math.Min(System.Math.Max(sigmoid_predict(dec_values[k], model.probA[k], model.probB[k]), min_prob), 1 - min_prob);
pairwise_prob[j][i] = 1 - pairwise_prob[i][j];
k++;
}
multiclass_probability(nr_class, pairwise_prob, prob_estimates);
int prob_max_idx = 0;
for (i = 1; i < nr_class; i++)
if (prob_estimates[i] > prob_estimates[prob_max_idx])
prob_max_idx = i;
return model.label[prob_max_idx];
}
else
return svm_predict(model, x);
}
//UPGRADE_NOTE: Final was removed from the declaration of 'svm_type_table'. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
internal static readonly System.String[] svm_type_table = new System.String[]{"c_svc", "nu_svc", "one_class", "epsilon_svr", "nu_svr"};
//UPGRADE_NOTE: Final was removed from the declaration of 'kernel_type_table'. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
internal static readonly System.String[] kernel_type_table = new System.String[]{"linear", "polynomial", "rbf", "sigmoid"};
public static void svm_save_model(System.String model_file_name, svm_model model)
{
//UPGRADE_TODO: Class 'java.io.DataOutputStream' was converted to 'System.IO.BinaryWriter' which has a different behavior. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1073_javaioDataOutputStream_3"'
//UPGRADE_TODO: Constructor 'java.io.FileOutputStream.FileOutputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1073_javaioFileOutputStreamFileOutputStream_javalangString_3"'
/* Original System.IO.BinaryWriter fp = new System.IO.BinaryWriter(new System.IO.FileStream(model_file_name, System.IO.FileMode.Create));*/
System.IO.StreamWriter fp = new System.IO.StreamWriter(new System.IO.FileStream(model_file_name, System.IO.FileMode.Create), System.Text.Encoding.Default);
svm_parameter param = model.param;
fp.Write("svm_type " + svm_type_table[param.svm_type] + "\n");
fp.Write("kernel_type " + kernel_type_table[param.kernel_type] + "\n");
if (param.kernel_type == svm_parameter.POLY)
fp.Write("degree " + param.degree + "\n");
if (param.kernel_type == svm_parameter.POLY || param.kernel_type == svm_parameter.RBF || param.kernel_type == svm_parameter.SIGMOID)
fp.Write("gamma " + param.gamma + "\n");
if (param.kernel_type == svm_parameter.POLY || param.kernel_type == svm_parameter.SIGMOID)
fp.Write("coef0 " + param.coef0 + "\n");
int nr_class = model.nr_class;
int l = model.l;
fp.Write("nr_class " + nr_class + "\n");
fp.Write("total_sv " + l + "\n");
{
fp.Write("rho");
for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
fp.Write(" " + model.rho[i]);
fp.Write("\n");
}
if (model.label != null)
{
fp.Write("label");
for (int i = 0; i < nr_class; i++)
fp.Write(" " + model.label[i]);
fp.Write("\n");
}
if (model.probA != null)
// regression has probA only
{
fp.Write("probA");
for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
fp.Write(" " + model.probA[i]);
fp.Write("\n");
}
if (model.probB != null)
{
fp.Write("probB");
for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
fp.Write(" " + model.probB[i]);
fp.Write("\n");
}
if (model.nSV != null)
{
fp.Write("nr_sv");
for (int i = 0; i < nr_class; i++)
fp.Write(" " + model.nSV[i]);
fp.Write("\n");
}
fp.Write("SV\n");
double[][] sv_coef = model.sv_coef;
svm_node[][] SV = model.SV;
for (int i = 0; i < l; i++)
{
for (int j = 0; j < nr_class - 1; j++)
fp.Write(sv_coef[j][i] + " ");
svm_node[] p = SV[i];
for (int j = 0; j < p.Length; j++)
fp.Write(p[j].index + ":" + p[j].value_Renamed + " ");
fp.Write("\n");
}
fp.Close();
}
private static double atof(System.String s)
{
return System.Double.Parse(s);
}
private static int atoi(System.String s)
{
return System.Int32.Parse(s);
}
public static svm_model svm_load_model(System.String model_file_name)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1092_3"'
//UPGRADE_WARNING: At least one expression was used more than once in the target code. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1181_3"'
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1073_3"'
/*Original System.IO.StreamReader fp = new System.IO.StreamReader(new System.IO.StreamReader(model_file_name, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(model_file_name, System.Text.Encoding.Default).CurrentEncoding);*/
System.IO.StreamReader fp = new System.IO.StreamReader(new System.IO.FileStream(model_file_name, System.IO.FileMode.Open), System.Text.Encoding.Default);
// read parameters
svm_model model = new svm_model();
svm_parameter param = new svm_parameter();
model.param = param;
model.rho = null;
model.probA = null;
model.probB = null;
model.label = null;
model.nSV = null;
while (true)
{
System.String cmd = fp.ReadLine();
System.String arg = cmd.Substring(cmd.IndexOf((System.Char) ' ') + 1);
if (cmd.StartsWith("svm_type"))
{
int i;
for (i = 0; i < svm_type_table.Length; i++)
{
if (arg.IndexOf(svm_type_table[i]) != - 1)
{
param.svm_type = i;
break;
}
}
if (i == svm_type_table.Length)
{
System.Console.Error.Write("unknown svm type.\n");
return null;
}
}
else if (cmd.StartsWith("kernel_type"))
{
int i;
for (i = 0; i < kernel_type_table.Length; i++)
{
if (arg.IndexOf(kernel_type_table[i]) != - 1)
{
param.kernel_type = i;
break;
}
}
if (i == kernel_type_table.Length)
{
System.Console.Error.Write("unknown kernel function.\n");
return null;
}
}
else if (cmd.StartsWith("degree"))
param.degree = atof(arg);
else if (cmd.StartsWith("gamma"))
param.gamma = atof(arg);
else if (cmd.StartsWith("coef0"))
param.coef0 = atof(arg);
else if (cmd.StartsWith("nr_class"))
model.nr_class = atoi(arg);
else if (cmd.StartsWith("total_sv"))
model.l = atoi(arg);
else if (cmd.StartsWith("rho"))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.rho = new double[n];
SupportClass.Tokenizer st = new SupportClass.Tokenizer(arg);
for (int i = 0; i < n; i++)
model.rho[i] = atof(st.NextToken());
}
else if (cmd.StartsWith("label"))
{
int n = model.nr_class;
model.label = new int[n];
SupportClass.Tokenizer st = new SupportClass.Tokenizer(arg);
for (int i = 0; i < n; i++)
model.label[i] = atoi(st.NextToken());
}
else if (cmd.StartsWith("probA"))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.probA = new double[n];
SupportClass.Tokenizer st = new SupportClass.Tokenizer(arg);
for (int i = 0; i < n; i++)
model.probA[i] = atof(st.NextToken());
}
else if (cmd.StartsWith("probB"))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.probB = new double[n];
SupportClass.Tokenizer st = new SupportClass.Tokenizer(arg);
for (int i = 0; i < n; i++)
model.probB[i] = atof(st.NextToken());
}
else if (cmd.StartsWith("nr_sv"))
{
int n = model.nr_class;
model.nSV = new int[n];
SupportClass.Tokenizer st = new SupportClass.Tokenizer(arg);
for (int i = 0; i < n; i++)
model.nSV[i] = atoi(st.NextToken());
}
else if (cmd.StartsWith("SV"))
{
break;
}
else
{
System.Console.Error.Write("unknown text in model file\n");
return null;
}
}
// read sv_coef and SV
int m = model.nr_class - 1;
int l = model.l;
model.sv_coef = new double[m][];
for (int i = 0; i < m; i++)
{
model.sv_coef[i] = new double[l];
}
model.SV = new svm_node[l][];
for (int i = 0; i < l; i++)
{
System.String line = fp.ReadLine();
SupportClass.Tokenizer st = new SupportClass.Tokenizer(line, " \t\n\r\f:");
for (int k = 0; k < m; k++)
model.sv_coef[k][i] = atof(st.NextToken());
int n = st.Count / 2;
model.SV[i] = new svm_node[n];
for (int j = 0; j < n; j++)
{
model.SV[i][j] = new svm_node();
model.SV[i][j].index = atoi(st.NextToken());
model.SV[i][j].value_Renamed = atof(st.NextToken());
}
}
fp.Close();
return model;
}
public static System.String svm_check_parameter(svm_problem prob, svm_parameter param)
{
// svm_type
int svm_type = param.svm_type;
if (svm_type != svm_parameter.C_SVC && svm_type != svm_parameter.NU_SVC && svm_type != svm_parameter.ONE_CLASS && svm_type != svm_parameter.EPSILON_SVR && svm_type != svm_parameter.NU_SVR)
return "unknown svm type";
// kernel_type
int kernel_type = param.kernel_type;
if (kernel_type != svm_parameter.LINEAR && kernel_type != svm_parameter.POLY && kernel_type != svm_parameter.RBF && kernel_type != svm_parameter.SIGMOID)
return "unknown kernel type";
// cache_size,eps,C,nu,p,shrinking
if (param.cache_size <= 0)
return "cache_size <= 0";
if (param.eps <= 0)
return "eps <= 0";
if (svm_type == svm_parameter.C_SVC || svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR)
if (param.C <= 0)
return "C <= 0";
if (svm_type == svm_parameter.NU_SVC || svm_type == svm_parameter.ONE_CLASS || svm_type == svm_parameter.NU_SVR)
if (param.nu < 0 || param.nu > 1)
return "nu < 0 or nu > 1";
if (svm_type == svm_parameter.EPSILON_SVR)
if (param.p < 0)
return "p < 0";
if (param.shrinking != 0 && param.shrinking != 1)
return "shrinking != 0 and shrinking != 1";
if (param.probability != 0 && param.probability != 1)
return "probability != 0 and probability != 1";
if (param.probability == 1 && svm_type == svm_parameter.ONE_CLASS)
return "one-class SVM probability output not supported yet";
// check whether nu-svc is feasible
if (svm_type == svm_parameter.NU_SVC)
{
int l = prob.l;
int max_nr_class = 16;
int nr_class = 0;
int[] label = new int[max_nr_class];
int[] count = new int[max_nr_class];
int i;
for (i = 0; i < l; i++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int this_label = (int) prob.y[i];
int j;
for (j = 0; j < nr_class; j++)
if (this_label == label[j])
{
++count[j];
break;
}
if (j == nr_class)
{
if (nr_class == max_nr_class)
{
max_nr_class *= 2;
int[] new_data = new int[max_nr_class];
Array.Copy(label, 0, new_data, 0, label.Length);
label = new_data;
new_data = new int[max_nr_class];
Array.Copy(count, 0, new_data, 0, count.Length);
count = new_data;
}
label[nr_class] = this_label;
count[nr_class] = 1;
++nr_class;
}
}
for (i = 0; i < nr_class; i++)
{
int n1 = count[i];
for (int j = i + 1; j < nr_class; j++)
{
int n2 = count[j];
if (param.nu * (n1 + n2) / 2 > System.Math.Min(n1, n2))
return "specified nu is infeasible";
}
}
}
return null;
}
public static int svm_check_probability_model(svm_model model)
{
if (((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) && model.probA != null && model.probB != null) || ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) && model.probA != null))
return 1;
else
return 0;
}
}
}