#include "svm_model.h"
#include "math.h"

#define MAX(x, y) ((x)>(y))?(x):(y)
#define MIN(x, y) ((x)<(y))?(x):(y)

#define KERNEL_UNKNOWN 0
#define KERNEL_LINEAR  1
#define KERNEL_POLYNOMIAL 2
#define KERNEL_RBF 3

typedef float (*KernelHandle)(const float *xi, const float *x, const Model *model);

static int32_t g_label[NR_CLASS] = {
1, 0
};

static float g_coefs[NR_CLASS-1][TOTAL_SV] = {
{
0.047765f,0.193741f,0.193728f,0.020037f,0.028449f,0.009899f,0.193736f,0.193686f,0.193680f,0.193658f,
0.011327f,0.010485f,0.015459f,0.047445f,0.102630f,0.193595f,0.193621f,0.020161f,0.097134f,0.024713f,
0.020782f,0.138753f,0.182116f,0.008570f,0.007608f,0.169169f,0.015109f,0.193612f,0.029349f,0.193595f,
0.010708f,0.056469f,0.181776f,0.193588f,0.185042f,0.028767f,0.193596f,0.048343f,0.097243f,0.013492f,
0.193819f,0.041889f,0.193604f,0.028989f,0.193515f,0.117248f,0.107213f,0.048071f,0.022198f,0.043433f,
0.193603f,0.014046f,0.021226f,0.193700f,0.116852f,0.193838f,0.009825f,0.141092f,0.009385f,0.012850f,
0.193644f,0.047386f,0.010491f,0.082054f,0.138907f,0.008682f,0.193695f,0.029892f,0.193640f,0.193725f,
0.193749f,0.013211f,0.118761f,0.047993f,0.016971f,0.043245f,0.171411f,0.007124f,0.193738f,0.193744f,
0.188871f,0.010448f,0.075379f,0.193278f,0.193423f,0.193368f,0.047122f,0.193680f,0.050551f,0.193693f,
0.193750f,0.020099f,0.193810f,0.193829f,0.187626f,0.012608f,0.193623f,0.193639f,0.038979f,0.021526f,
0.193611f,0.089685f,0.083959f,0.045748f,0.049323f,0.046950f,0.014118f,0.185050f,0.193623f,0.009381f,
0.021500f,0.193634f,0.118163f,0.028525f,0.193669f,0.166976f,0.193336f,0.011352f,0.193739f,0.193695f,
0.193598f,0.193626f,0.010494f,0.013779f,0.193605f,0.193606f,0.010365f,0.193616f,0.022837f,0.013647f,
0.193612f,0.193606f,0.193614f,0.012969f,0.193622f,0.193623f,0.189224f,0.193626f,0.048151f,0.193614f,
0.015475f,0.138343f,0.047206f,0.017081f,0.096992f,0.184407f,0.193621f,0.183833f,0.193663f,0.009239f,
0.037953f,0.193741f,0.193624f,0.117151f,0.193622f,0.193680f,0.193616f,0.012191f,0.056366f,0.193668f,
0.028452f,0.020093f,0.047970f,0.193682f,0.019594f,0.012933f,0.193627f,0.097082f,0.193617f,0.171412f,
0.073546f,0.193623f,0.193520f,0.178993f,0.082168f,0.193648f,0.193628f,0.018029f,0.010973f,0.039229f,
0.193324f,0.193794f,0.016484f,0.091531f,0.089775f,0.029770f,0.193620f,0.020971f,0.097222f,0.011153f,
0.193145f,0.020633f,0.089676f,0.144743f,0.169382f,0.171519f,0.047832f,0.011108f,0.057249f,0.193791f,
0.029323f,0.193607f,0.065518f,0.011641f,0.012648f,0.081918f,0.019831f,0.048022f,0.193666f,0.193651f,
0.023991f,0.193674f,0.170988f,0.193635f,0.080236f,0.028429f,0.068589f,0.193616f,0.013571f,0.010662f,
0.097132f,0.193651f,0.035883f,0.096855f,0.193670f,0.097208f,0.193750f,0.047835f,0.047910f,0.132412f,
0.193636f,0.089945f,0.193652f,0.193683f,0.193662f,0.048518f,0.193658f,0.028488f,0.193370f,0.039273f,
0.193658f,0.193696f,0.193686f,0.193690f,0.171427f,0.089100f,0.193741f,0.193679f,0.015436f,0.193723f,
0.022055f,0.184420f,0.029467f,0.027604f,0.180594f,0.020088f,0.116536f,0.082522f,0.193787f,0.012756f,
0.193727f,0.193742f,0.096988f,0.193736f,0.193730f,0.138601f,0.029474f,0.125675f,0.048018f,0.138490f,
0.014584f,0.193736f,0.193783f,0.191118f,0.013163f,0.020935f,0.055785f,0.189675f,0.138534f,0.097099f,
0.193026f,0.193682f,0.010569f,0.184235f,0.189759f,0.080724f,0.138568f,0.193614f,0.193655f,0.097020f,
0.029558f,0.193644f,0.193656f,0.193627f,0.068325f,0.184309f,0.037989f,0.011482f,0.144008f,0.179297f,
0.012109f,0.009769f,0.193585f,0.010437f,0.193603f,0.184419f,0.193683f,0.193622f,0.017022f,0.191501f,
0.126087f,0.193602f,0.193600f,0.193619f,0.193613f,0.104430f,0.082135f,0.010936f,0.193579f,0.193723f,
0.016076f,0.193592f,0.096961f,0.169320f,0.089681f,0.009240f,0.193739f,0.193576f,0.096806f,0.012794f,
0.193583f,0.193598f,0.193600f,0.015420f,0.193589f,0.009862f,0.193599f,0.007939f,0.193596f,0.193597f,
0.193612f,0.193621f,0.005467f,0.193616f,0.193508f,0.039264f,0.039332f,0.009834f,0.089616f,0.193592f,
0.193692f,0.193600f,0.193601f,0.139035f,0.089318f,0.056096f,0.017029f,0.015777f,0.028624f,0.193608f,
0.028130f,0.193606f,0.008991f,0.193606f,0.193621f,0.181341f,0.193633f,0.193629f,0.106518f,0.089566f,
0.007590f,0.169428f,0.193610f,0.193599f,0.082454f,0.193641f,0.193638f,0.185757f,0.193619f,0.042736f,
0.117261f,0.028263f,0.193654f,0.019268f,0.089811f,0.193669f,0.193660f,0.047555f,0.185149f,0.193689f,
0.185080f,0.193680f,0.020877f,0.018991f,0.089940f,0.193606f,0.007431f,0.193738f,0.193739f,0.193746f,
0.006682f,0.193616f,0.013761f,0.135997f,0.193740f,0.028086f,0.138641f,0.019535f,0.011726f,0.056663f,
0.009049f,0.193694f,0.193752f,0.193750f,0.097395f,0.014340f,0.012195f,0.193749f,0.011406f,0.193788f,
0.193742f,0.089109f,0.193743f,0.090021f,0.009116f,0.083277f,0.184596f,0.005862f,0.193728f,0.180470f,
0.090053f,0.193585f,0.019449f,0.193734f,0.193723f,0.169321f,0.184075f,0.012707f,0.029593f,0.169322f,
0.009972f,0.116670f,0.180646f,0.193679f,0.193674f,0.054647f,0.193684f,0.020417f,0.193674f,0.193701f,
0.184346f,0.015536f,0.028785f,0.030203f,0.193615f,0.010883f,0.013079f,0.193614f,0.055802f,0.193674f,
0.018260f,0.042536f,0.193665f,0.090123f,0.193273f,0.193601f,0.193280f,0.089515f,0.184397f,0.193304f,
0.020833f,0.193324f,0.116815f,0.089969f,0.184354f,0.193330f,0.010928f,0.018732f,0.193404f,0.010358f,
0.116941f,0.193814f,0.008748f,0.056373f,0.193420f,0.047493f,0.193639f,0.193601f,0.193622f,0.193659f,
0.193689f,0.193365f,0.193735f,0.096364f,0.096541f,0.193793f,0.193807f,0.193750f,0.037705f,0.193804f,
0.027765f,0.193829f,0.193837f,0.193619f,0.193841f,0.193654f,0.193844f,0.047327f,0.041419f,0.193572f,
0.029912f,0.193860f,0.010821f,0.046410f,0.193889f,0.138636f,0.046966f,0.047755f,0.169277f,0.065545f,
0.008652f,0.189627f,0.009956f,0.091727f,0.020792f,0.185143f,0.056498f,0.193739f,0.138374f,0.125977f,
0.193854f,0.029598f,0.193824f,0.011317f,0.046998f,0.046876f,0.019746f,0.089376f,0.019223f,0.193814f,
0.018358f,0.193836f,0.194144f,0.017584f,0.011814f,0.097079f,0.193597f,0.193811f,0.193815f,0.013101f,
0.007997f,0.012880f,0.193829f,0.184340f,0.017344f,0.038714f,0.193821f,0.193567f,0.193631f,0.056871f,
0.193828f,0.008069f,0.028616f,0.028637f,0.193778f,0.193798f,0.193787f,0.193638f,0.193770f,0.185243f,
0.046545f,0.017200f,0.020334f,0.193764f,0.029731f,0.009482f,0.193702f,0.096663f,0.193740f,0.185124f,
0.089263f,0.193739f,0.028533f,0.188955f,0.018598f,0.193618f,0.097002f,0.193360f,0.193619f,0.193752f,
0.097194f,0.038852f,0.193749f,0.048594f,0.193757f,0.193748f,0.193197f,0.193670f,0.046804f,0.010484f,
0.193639f,0.193631f,0.008424f,0.193787f,0.138737f,0.029001f,0.193891f,0.011483f,0.009351f,0.193280f,
0.193726f,0.193747f,0.193731f,0.193602f,0.047944f,0.118468f,0.067753f,0.020057f,0.193350f,0.008488f,
0.008867f,0.161263f,0.193617f,0.193619f,0.193643f,0.193584f,0.193579f,0.193689f,0.193619f,0.193770f,
0.125886f,0.193641f,0.193587f,0.014148f,0.007764f,0.193640f,0.009672f,0.193591f,0.193656f,0.116816f,
0.193434f,0.193642f,0.047528f,0.193728f,0.018132f,0.020128f,0.089192f,0.193656f,0.193648f,0.193656f,
0.089976f,0.138788f,0.193603f,0.182532f,0.193665f,0.193686f,0.012917f,0.193669f,0.079733f,0.015986f,
0.193684f,0.008466f,0.193641f,0.193656f,0.017886f,0.012177f,0.193667f,0.193728f,0.193624f,0.015670f,
0.097089f,0.138571f,0.185085f,0.011275f,0.089536f,0.193640f,0.193657f,0.193660f,0.089211f,0.018900f,
0.193642f,0.193827f,0.047939f,0.193818f,0.089006f,0.014796f,0.193874f,0.193822f,0.193840f,0.169335f,
0.013632f,0.016835f,0.018346f,0.184653f,0.193752f,0.193650f,0.008688f,0.012922f,0.193699f,0.193642f,
0.193670f,0.193661f,0.193591f,0.193613f,0.046857f,0.006453f,0.041825f,0.021004f,0.117161f,0.011345f,
0.009471f,0.193437f,0.193626f,0.117210f,0.056376f,0.011232f,0.185082f,0.047521f,0.047699f,0.007114f,
0.097138f,0.007977f,0.017582f,0.193675f,0.193667f,0.193634f,0.011030f,0.193628f,0.193519f,0.080722f,
0.089822f,0.193623f,0.189346f,0.014431f,0.193658f,0.138733f,0.188823f,0.089401f,0.006881f,0.193629f,
0.193831f,0.117189f,0.193827f,0.193340f,0.193891f,0.017040f,0.046991f,0.193874f,0.171510f,0.193763f,
0.008507f,0.193627f,0.096989f,0.089585f,0.169195f,0.009495f,0.005913f,0.193738f,0.015916f,0.193725f,
0.193669f,0.116787f,0.193621f,0.193621f,0.185000f,0.020016f,0.036852f,0.184339f,0.089736f,0.016357f,
0.046901f,0.009586f,0.096726f,0.010232f,0.089406f,0.008265f,0.193745f,0.193600f,0.193072f,0.125941f,
0.184017f,0.057249f,0.007230f,0.008543f,0.048305f,0.012939f,0.193657f,0.029039f,0.008202f,0.132940f,
0.193626f,0.193668f,0.126239f,0.020602f,0.193862f,0.011258f,0.193826f,0.193680f,0.193688f,0.193647f,
0.193631f,0.193613f,0.007785f,0.018962f,0.047047f,0.193618f,0.028314f,0.068679f,0.184980f,0.193613f,
0.039187f,0.193659f,0.188762f,0.193620f,0.193873f,0.193658f,0.014254f,0.012869f,0.193625f,0.193614f,
0.193684f,0.193618f,0.096518f,0.056375f,0.009405f,0.193728f,0.193669f,0.028396f,0.011001f,0.144626f,
0.193609f,0.007842f,0.011310f,0.089422f,0.047207f,0.193669f,0.007302f,0.193600f,0.141638f,0.120281f,
0.010809f,0.193591f,0.117288f,0.018704f,0.193607f,0.011248f,0.007123f,0.161068f,0.183971f,0.007882f,
0.193806f,0.089231f,0.006836f,0.056087f,0.193415f,0.193698f,0.009700f,0.018410f,0.089591f,0.009519f,
0.193734f,0.193603f,0.193653f,0.193671f,0.013637f,0.193620f,0.193620f,0.064901f,0.193646f,0.193749f,
0.011177f,0.193629f,0.193632f,0.037512f,0.088756f,0.138806f,0.193278f,0.193814f,0.017306f,0.193784f,
0.144262f,0.097226f,0.193791f,0.089597f,0.017961f,0.185844f,0.193752f,0.185068f,0.193676f,0.189188f,
0.010449f,0.193739f,0.005348f,0.096991f,0.185218f,0.007902f,0.193641f,0.193273f,0.193660f,0.193600f,
0.096850f,0.193636f,0.008976f,0.193613f,0.193733f,0.193607f,0.194018f,0.130453f,0.006425f,0.017428f,
0.028655f,0.193603f,0.169408f,0.099363f,0.193618f,0.009853f,0.161001f,0.193627f,0.139252f,0.193689f,
0.193602f,0.017771f,0.008786f,0.028889f,0.193686f,0.117053f,0.010001f,0.193610f,0.184297f,0.193445f,
0.096901f,0.193679f,0.193715f,0.193692f,0.047787f,0.125737f,0.193733f,0.046000f,0.007801f,0.117213f,
0.056892f,0.010596f,0.193727f,0.181335f,0.064512f,0.018467f,0.022288f,0.193687f,0.193643f,0.126496f,
0.004098f,0.193654f,0.193667f,0.193591f,0.125567f,0.117181f,0.095595f,0.047511f,0.008192f,0.010285f,
0.193697f,0.185066f,0.007894f,0.193596f,0.009508f,0.193597f,0.193677f,0.193665f,0.193806f,0.096278f,
0.184325f,0.193659f,0.193588f,0.169457f,0.193587f,0.020528f,0.138575f,0.193579f,0.017494f,0.184242f,
0.029431f,0.193733f,0.008612f,0.185120f,0.193421f,0.169622f,0.184465f,0.193593f,0.193292f,0.096464f,
0.008435f,0.016621f,0.007031f,0.193633f,0.008949f,0.027520f,0.015638f,0.153062f,0.193603f,0.193640f,
0.193737f,0.013462f,0.193618f,0.116759f,0.193575f,0.138733f,0.018959f,0.068471f,0.193686f,0.193714f,
0.193656f,0.193651f,0.193739f,0.193508f,0.193741f,0.027557f,0.007247f,0.138728f,0.193597f,0.021128f,
0.193710f,0.183693f,0.193526f,0.008958f,0.193702f,0.028934f,0.194071f,0.185084f,0.097039f,0.185245f,
0.184679f,0.013576f,0.009889f,0.193397f,0.046275f,0.194137f,0.193340f,0.185082f,0.020643f,0.019122f,
0.193635f,0.193578f,0.194091f,0.184461f,0.171652f,0.194071f,0.194012f,0.193673f,0.185133f,0.010062f,
0.009550f,0.194000f,0.193636f,0.081399f,0.089792f,0.184790f,0.180514f,0.193808f,0.193738f,0.193705f,
0.169445f,0.193692f,0.007790f,0.193688f,0.047203f,0.008198f,0.117678f,0.030702f,0.047043f,0.185720f,
0.188285f,0.193641f,0.096914f,0.193616f,0.030273f,0.173967f,0.193622f,0.193624f,0.193624f,0.064851f,
0.193660f,0.007441f,0.193623f,0.089596f,0.193620f,0.193846f,0.185083f,0.096358f,0.193629f,0.096518f,
0.193672f,0.193657f,0.193675f,0.193677f,0.193681f,0.096675f,0.185111f,0.118829f,0.193680f,0.193370f,
0.193726f,0.193802f,0.193733f,0.046368f,0.117169f,0.185113f,0.193736f,0.185564f,0.193732f,0.011281f,
0.193683f,0.193688f,0.027121f,0.009716f,0.193732f,0.089674f,0.007875f,0.193689f,0.047848f,0.089545f,
0.193684f,0.193688f,0.096829f,0.013566f,0.096392f,0.118709f,0.193702f,0.009579f,0.096337f,0.171563f,
0.193754f,0.018624f,0.009668f,0.009859f,0.006964f,0.193614f,0.185162f,0.193501f,0.193688f,0.004664f,
0.008630f,0.193693f,0.193690f,0.193683f,0.048070f,0.193663f,0.193682f,0.011799f,0.048283f,0.193683f,
0.008052f,0.014589f,0.069381f,0.193782f,0.089267f,0.193198f,0.185063f,0.097060f,0.047163f,0.193252f,
0.007615f,0.193559f,0.029917f,0.014930f,0.007022f,0.193614f,0.118348f,0.009663f,0.193669f,0.193704f,
0.116960f,0.184299f,0.193837f,0.048103f,0.096823f,0.194148f,0.009985f,0.193666f,0.029039f,0.193663f,
0.135961f,0.171561f,0.020042f,0.194151f,0.018278f,0.193737f,0.089521f,0.007205f,0.047622f,0.009439f,
0.193804f,0.012188f,0.194164f,0.046954f,0.013892f,0.161385f,0.193738f,0.193738f,0.020129f,0.193739f,
0.185183f,0.193692f,0.193745f,0.007535f,0.193347f,0.193754f,0.016667f,0.193662f,0.011736f,0.010893f,
0.007929f,0.110254f,0.020077f,0.017870f,0.169029f,0.097052f,0.193737f,0.193737f,0.193629f,0.096652f,
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0.193192f,0.039779f,0.131280f,0.010439f,0.015386f,0.009370f,0.193694f,0.117444f,0.029655f,0.161032f,
0.193695f,0.018833f,0.193789f,0.009636f,0.021460f,0.056841f,0.193449f,0.193603f,0.193736f,0.005211f,
0.193371f,0.126193f,0.193724f,0.010767f,0.185095f,0.193722f,0.193714f,0.038687f,0.013067f,0.193618f,
0.010228f,0.009100f,0.031333f,0.027605f,0.056197f,0.011935f,0.012988f,0.018677f,0.193726f,0.193735f,
0.194157f,0.193701f,0.184339f,0.193711f,0.193700f,0.184316f,0.194100f,0.185219f,0.080228f,0.009568f,
0.009294f,0.019186f,0.193717f,0.193878f,0.056433f,0.048500f,0.011703f,0.138671f,0.193757f,0.193461f,
0.012719f,0.193650f,0.193682f,0.126267f,0.185214f,0.193624f,0.027609f,0.012409f,0.193694f,0.193633f,
0.193663f,0.193639f,0.126234f,0.193646f,0.169687f,0.018067f,0.097307f,0.126157f,0.009945f,0.014182f,
0.046591f,0.193851f,0.138675f,0.002569f,0.193668f,0.009317f,0.047337f,0.193658f,0.018320f,0.012129f,
0.193737f,0.193702f,0.193709f,0.144056f,0.185120f,0.046314f,0.180777f,0.193704f,0.193704f,0.020330f,
0.139111f,0.193700f,0.018940f,0.010048f,0.011686f,0.009583f,0.012811f,0.007811f,0.193670f,0.020044f,
0.089711f,0.118785f,0.016384f,0.058377f,0.189307f,0.015568f,0.193703f,0.111346f,0.010391f,0.067571f,
0.193840f,0.009833f,0.193606f,0.007978f,0.183425f,0.193703f,0.028331f,0.009022f,0.012625f,0.193597f,
0.048146f,0.011544f,0.193692f,0.193664f,0.194147f,0.138733f,0.193737f,0.125679f,0.006134f,0.193746f,
0.126071f,0.097718f,0.138699f,0.097032f,0.193696f,0.096557f,0.016102f,0.193733f,0.019105f,0.193738f,
0.193750f,0.193679f,0.010803f,0.078684f,0.193725f,0.007726f,0.014352f,0.193705f,0.117887f,0.089002f,
0.008627f,0.096480f,0.184289f,0.051477f,0.015179f,0.072100f,0.193717f,0.193640f,0.018481f,0.193663f,
0.193733f,0.193683f,0.193647f,0.015180f,0.029659f,0.193695f,0.193658f,0.193677f,0.011497f,0.193639f,
0.193624f,0.193619f,0.189402f,0.008077f,0.185196f,0.193633f,0.192296f,0.193674f,0.013564f,0.011075f,
0.193600f,0.193619f,0.088776f,0.193652f,0.193621f,0.185960f,0.193661f,0.193617f,0.193601f,0.193619f,
0.193598f,0.048216f,0.144165f,0.193574f,0.193604f,0.152349f,0.193612f,0.193619f,0.193670f,0.010789f,
0.009754f,0.010001f,0.010699f,0.008604f,0.009185f,0.012045f,0.193638f,0.011506f,0.193655f,0.006835f,
0.138934f,0.028628f,0.193616f,0.193424f,0.184395f,0.193651f,0.070986f,0.193747f,0.013522f,0.193722f,
0.193614f,0.193704f,0.189314f,0.193677f,0.193608f,0.193680f,0.169425f,0.018000f,0.089640f,0.008796f,
0.020408f,0.089858f,0.193687f,0.169215f,0.027728f,0.038123f,0.193607f,0.185120f,0.193666f,0.138807f,
0.009224f,0.134585f,0.096935f,0.014377f,0.096403f,0.193606f,0.193626f,0.193734f,0.007720f,0.193710f,
0.016458f,0.193530f,0.096690f,0.003270f,0.193621f,0.012861f,0.117203f,0.097356f,0.065055f,0.047170f,
0.118588f,0.038386f,0.182627f,0.017682f,0.019610f,0.015566f,0.193626f,0.012404f,0.096883f,0.116440f,
0.193760f,0.047763f,0.018922f,0.185690f,0.023548f,0.143695f,0.193625f,0.007800f,0.169274f,0.109729f,
0.096513f,0.126488f,0.132631f,0.193591f,0.080416f,0.193620f,0.015679f,0.009270f,0.193617f,0.193593f,
0.193603f,0.068036f,0.138978f,0.193662f,0.193733f,0.012282f,0.010370f,0.193602f,0.193623f,0.056276f,
0.193838f,0.193827f,0.096443f,0.097890f,0.081750f,0.169776f,0.193598f,0.193863f,0.046968f,0.089147f,
0.193682f,0.097202f,0.018746f,0.193615f,0.138603f,0.009177f,0.004702f,0.193764f,0.008926f,0.184409f,
0.096850f,0.072193f,0.184478f,0.193559f,0.011111f,0.039246f,0.193554f,0.097993f,0.185074f,0.193600f,
0.193694f,0.193724f,0.012185f,0.016334f,0.169072f,0.010502f,0.193686f,0.011062f,0.193623f,0.013811f,
0.028353f,0.184298f,0.193628f,0.193656f,0.132613f,0.193680f,0.011122f,0.193827f,0.193747f,0.011143f,
0.089370f,0.068855f,0.193618f,0.029677f,0.007183f,0.193643f,0.185214f,0.193594f,0.193598f,0.193601f,
0.193741f,0.193822f,0.012948f,0.193664f,0.006468f,0.007671f,0.193670f,0.193609f,0.007943f,0.193736f,
0.193805f,0.193888f,0.047196f,0.193872f,0.193514f,0.193750f,0.055539f,0.193641f,0.048779f,0.193626f,
0.193784f,0.193679f,0.089710f,0.047857f,0.037689f,0.193753f,0.193696f,0.184428f,0.055992f,0.051216f,
0.089013f,0.166421f,0.189296f,0.047935f,0.184204f,0.004863f,0.116925f,0.193782f,0.193640f,0.193347f,
0.194018f,0.185054f,0.193652f,0.193532f,0.097369f,0.193874f,0.193827f,0.193868f,0.193508f,0.139026f,
0.046992f,0.117349f,0.097072f,0.169315f,0.193276f,0.193404f,0.007623f,0.117629f,0.193689f,0.193815f,
0.193461f,0.046764f,0.046840f,0.089077f,0.009907f,0.027738f,0.185287f,0.180701f,0.193376f,0.193372f,
0.011664f,0.184413f,0.193311f,0.014723f,0.014411f,0.010889f,0.011273f,0.096447f,0.029415f,0.193292f,
0.193617f,0.193267f,0.046765f,0.012538f,0.193253f,0.009381f,0.117151f,0.193252f,0.193216f,0.193198f,
0.193794f,0.193168f,0.055911f,0.193663f,0.193670f,0.193604f,0.011007f,0.193672f,0.011375f,0.193673f,
0.193602f,0.046520f,0.007629f,0.193684f,0.193686f,0.193686f,0.194068f,0.193689f,0.193689f,0.193697f,
0.097235f,0.138482f,0.193675f,0.193700f,0.138578f,0.193837f,0.193700f,0.193700f,0.018655f,0.014510f,
0.138759f,0.193702f,0.020360f,0.193705f,0.070444f,0.029477f,0.007257f,0.029245f,0.193709f,0.029082f,
0.193709f,0.010693f,0.047179f,0.193714f,0.193715f,0.193741f,0.010893f,0.007564f,0.047244f,0.089433f,
0.193740f,0.101743f,0.193742f,0.011055f,0.193736f,0.088728f,0.193737f,0.009835f,0.193739f,0.160981f,
0.042998f,0.193677f,0.193737f,0.193741f,0.046256f,0.006137f,0.028532f,0.193740f,0.073777f,0.193750f,
0.008382f,0.193755f,0.014343f,0.055939f,0.046566f,0.042440f,0.097110f,0.005719f,0.079738f,0.046830f,
0.017217f,0.048055f,0.002103f,0.193754f,0.193751f,0.041363f,0.185283f,0.018565f,0.193567f,0.153977f,
0.193752f,0.193733f,0.027333f,0.020220f,0.193878f,0.006526f,0.003909f,0.027238f,0.193737f,0.193739f,
0.080673f,0.012804f,0.171296f,0.010744f,0.117006f,0.169209f,0.028306f,0.012226f,0.008991f,0.193732f,
0.071764f,0.046712f,0.091153f,0.169436f,0.008010f,0.017773f,0.007592f,0.193727f,0.012604f,0.193727f,
0.097194f,0.193720f,0.008651f,0.193884f,0.011532f,0.193705f,0.193746f,0.117484f,0.039318f,0.039325f,
0.047043f,0.193701f,0.011777f,0.012387f,0.193688f,0.193681f,0.169240f,0.019536f,0.109786f,0.012940f,
0.193681f,0.017457f,0.193676f,
}, 

};

static float g_SVs[FEATURE_SIZE*TOTAL_SV] = {
6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6366.0, 1402.0, 1.0, 1401.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0, 6364.0, 1402.0, 1.0, 1400.0,
17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 17.0, 1.0, 3.0, 34.0, 3.0, 7.0, 34.0, 3.0, 7.0, 34.0, 3.0, 7.0, 34.0, 3.0, 7.0,
11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 11.0, 2.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0, 269.0, 1.0, 1.0, 60.0,
28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 28.0, 1.0, 5.0, 6.0, 29.0, 1.0, 5.0, 6.0, 29.0, 1.0, 5.0, 6.0, 29.0, 1.0, 5.0, 6.0,
6409.0, 1411.0, 1.0, 1409.0, 6409.0, 1411.0, 1.0, 1409.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0, 6410.0, 1412.0, 1.0, 1410.0,
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};

static float g_rhos[NR_CLASSIFER(NR_CLASS)] = {
0.193666f
};

static int32_t g_nr_sv[NR_CLASS] = {

};

static float g_probA[NR_CLASSIFER(NR_CLASS)] = {

};

static float g_probB[NR_CLASSIFER(NR_CLASS)] = {

};

static float sigmoid_predict(float decision_value, float A, float B)
{
	float fApB = decision_value*A+B;
	// 1-p used later; avoid catastrophic cancellation
	if (fApB >= 0)
		return expf(-fApB)/(1.0+expf(-fApB));
	else
		return 1.0/(1+expf(fApB)) ;
}

static float linear_kernel(const float *xi, const float *x, const Model *model)
{
    float sum = 0.f;
    int i = 0;
    int len = model->vec_dim;
    for (i = 0; i < len; i++)
    {
        sum += xi[i] * x[i];
    }
    return sum;
}

static float polynomial_kernel(const float *xi, const float *x, const Model *model)
{
    float sum = 0.f;
    int i = 0;
    int len = model->vec_dim;
    float res = 0;

    for (i = 0; i < len; i++)
    {
        sum += xi[i] * x[i];
    }

    res = (model->gamma * sum + model->coef0);
    return res * res;
}

static float rbf_kernel(const float *xi, const float *x, const Model *model)
{
    float sum = 0.f;
    int i = 0;
    int len = model->vec_dim;
    float res = 0.;
    float temp = 0.;
    for (i = 0; i < len; i++)
    {
        temp = xi[i] - x[i];
        sum += temp*temp;
    }

    res = exp(-model->gamma*sum);
    return res;
}

void init_svm_params(Model *svm_model)
{
    svm_model->kernel_type = KERNEL_RBF;
    svm_model->degree   = 3;
    svm_model->gamma    = 0.1;
    svm_model->coef0    = 0;
    svm_model->nr_class = NR_CLASS;
    svm_model->nr_sv    = g_nr_sv;
    svm_model->total_sv = TOTAL_SV;
    svm_model->rhos     = g_rhos;
    svm_model->label    = g_label;
    svm_model->probA    = g_probA;
    svm_model->probB    = g_probB;

    // support vectors
    svm_model->coefs    = g_coefs;
    svm_model->vec_dim  = 200;
    svm_model->SVs      = g_SVs;
}


float predict(const Model *svm_model, const float* x, int len)
{
    int i = 0;
    float sum = 0.f;
    KernelHandle kernel;
    if (len != svm_model->vec_dim)
    {
        // len == feature num == vector dim
        return 0;
    }
    switch (svm_model->kernel_type)
    {
    case KERNEL_LINEAR:
        kernel = linear_kernel;
        break;
    case KERNEL_POLYNOMIAL:
        kernel = polynomial_kernel;
        break;
    case KERNEL_RBF:
        kernel = rbf_kernel;
        break;
    default:
        return 0;
    }

    for (i = 0; i < svm_model->total_sv; i++)
    {
        float* xi = svm_model->SVs + i * svm_model->vec_dim;
        sum += svm_model->coefs[0][i] * kernel(xi, x, svm_model);
    }

    return sum-svm_model->rhos[0];
}

float predict_probability(const Model *svm_model, const float* x, int len)
{
    float score = predict(svm_model, x, len);
    return sigmoid_predict(score, svm_model->probA[0], svm_model->probB[0]);
}

static int32_t svm_predict_values(const Model *svm_model, const float *x, float* dec_values)
{
    int i = 0;
    int j = 0;
    KernelHandle kernel;
    int nr_class = svm_model->nr_class;
    int l = svm_model->total_sv;
    float kvalue[TOTAL_SV];
    switch (svm_model->kernel_type)
    {
    case KERNEL_LINEAR:
        kernel = linear_kernel;
        break;
    case KERNEL_POLYNOMIAL:
        kernel = polynomial_kernel;
        break;
    case KERNEL_RBF:
        kernel = rbf_kernel;
        break;
    default:
        return -1;
    }

    for (i = 0; i < l; i++)
    {
        float* xi = svm_model->SVs + i * svm_model->vec_dim;
        kvalue[i] = kernel(xi, x, svm_model);
    }

    int start[NR_CLASS];
    start[0] = 0;
    for (i = 1; i < nr_class; i++)
        start[i] = start[i-1] + svm_model->nr_sv[i-1];

    int vote[NR_CLASS];
    for (i = 0; i < nr_class; i++)
        vote[i] = 0;

    int p = 0;
    for (i = 0; i < nr_class; i++)
    {
        for (j = i+1; j < nr_class; j++)
        {
            float sum = 0;
            int si = start[i];
            int sj = start[j];
            int ci = svm_model->nr_sv[i];
            int cj = svm_model->nr_sv[j];

            int k;
            float *coef1 = svm_model->coefs[j-1];
            float *coef2 = svm_model->coefs[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 -= svm_model->rhos[p];
            dec_values[p] = sum;

            if(dec_values[p] > 0)
                ++vote[i];
            else
                ++vote[j];
            p++;
        }
    }

    int vote_max_idx = 0;
    for(i=1;i<nr_class;i++)
        if(vote[i] > vote[vote_max_idx])
            vote_max_idx = i;

    return svm_model->label[vote_max_idx];
}

// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
static void multiclass_probability(int k, float (*r)[NR_CLASS], float *p)
{
    int t,j;
    int iter = 0, max_iter=MAX(100,k);
    float Q[k][k];
    float Qp[k];
    float pQp, eps=0.005/k;

    for (t = 0; t < k; t++)
    {
        p[t] = 1.0/k;  // Valid if k = 1
        Q[t][t] = 0;
        for (j = 0; j < t; j++)
        {
            Q[t][t] += r[j][t] * r[j][t];
            Q[t][j] = Q[j][t];
        }
        for (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 (j = 0; j < k; j++)
                Qp[t] += Q[t][j]*p[j];
            pQp += p[t]*Qp[t];
        }
        float max_error=0;
        for (t = 0; t < k; t++)
        {
            float error = fabsf(Qp[t]-pQp);
            if (error > max_error)
                max_error = error;
        }
        if (max_error < eps) break;

        for (t = 0; t < k; t++)
        {
            float 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 (j = 0; j < k; j++)
            {
                Qp[j] = (Qp[j] + diff*Q[t][j]) / (1 + diff);
                p[j] /= (1 + diff);
            }
        }
    }
    if (iter >= max_iter)
    {
#ifdef DEBUG_MODE
        printf("Exceeds max_iter in multiclass_prob\n");
#endif
    }
}

int32_t multi_predict(const Model* svm_model, const float* x, int len) {
    float dec_values[NR_CLASSIFER(NR_CLASS)];
    int label = svm_predict_values(svm_model, x, dec_values);
    return label;
}

int32_t multi_predict_probability(const Model *svm_model, const float* x, int len, float* scores_buf, int scores_buf_len)
{
    float dec_values[NR_CLASSIFER(NR_CLASS)];
    int32_t res = svm_predict_values(svm_model, x, dec_values);
    if (res < 0) return res;

    float min_prob=1e-7;
    float pairwise_prob[NR_CLASS][NR_CLASS];
    int i, j;
    int k=0;
    for (i = 0; i < NR_CLASS; i++)
    {
        for (j = i+1; j < NR_CLASS; j++)
        {
            pairwise_prob[i][j]=MIN(MAX(sigmoid_predict(dec_values[k],svm_model->probA[k],svm_model->probB[k]),min_prob),1-min_prob);
            pairwise_prob[j][i]=1-pairwise_prob[i][j];
            k++;
        }
    }
    float scores[NR_CLASS];
	multiclass_probability(NR_CLASS, pairwise_prob, scores);

    int prob_max_idx = 0;
    for (i = 1; i < NR_CLASS; i++)
        if(scores[i] > scores[prob_max_idx])
            prob_max_idx = i;
    if (scores_buf_len < NR_CLASS)
    {
        return -1;
    }
    for (i = 0; i < NR_CLASS; i++)
        scores_buf[i] = (float)scores[i];
    return svm_model->label[prob_max_idx];
}