diff --git a/E2T/E2T.ino b/E2T/E2T.ino
new file mode 100644
index 0000000..009b006
--- /dev/null
+++ b/E2T/E2T.ino
@@ -0,0 +1,234 @@
+#define KERNEL_GAMMA 0.001
+#include "iris.h"
+//#include "breast_cancer.h"
+//#include "digits.h"
+#define TOTAL_SAMPLES (POSITIVE_SAMPLES + NEGATIVE_SAMPLES)
+#pragma once
+
+namespace Edge2Train {
+    namespace SVM_train {
+        typedef float (*kernelFunction)(float *x, float *w, unsigned int dimension);
+        float linearKernel(float *x, float *w, unsigned int dimension) {
+            float sum = 0;
+            for (unsigned int i = 0; i < dimension; i++)
+                sum += x[i] * w[i];
+            return sum;   }
+
+#if defined(KERNEL_GAMMA)
+        float rbfKernel(float *x, float *w, unsigned int dimension) {
+            float sum = 0;
+            for(unsigned int i = 0; i < dimension; i++)
+                sum += (x[i] - w[i]) * (x[i] - w[i]);
+            return exp(-sum / (2 * KERNEL_GAMMA * KERNEL_GAMMA));    }
+#endif
+    } }
+
+namespace Edge2Train {
+    namespace SVM_train {
+        template<unsigned int D>
+        class SVMSMO {
+        public:
+            SVMSMO(kernelFunction kernel) :
+                    _kernel(kernel),
+                    _cache(NULL) {
+                _params = {
+                        .C = 1,
+                        .tol = 1e-4,
+                        .alphaTol = 1e-7,
+                        .maxIter = 10000,
+                        .passes = 10
+                };   }
+
+            void setC(float C) {
+                _params.C = C;  }
+
+            void setTol(float tol) {
+                _params.tol = tol;    }
+                
+            void setAlphaTol(float alphaTol) {
+                _params.alphaTol = alphaTol; }
+
+            void setMaxIter(unsigned int maxIter) {
+                _params.maxIter = maxIter;  }
+
+            void setPasses(unsigned int passes) {
+                _params.passes = passes;   }
+
+            void setKernelCache(float *cache) {
+                _cache = cache;  }
+
+            void fit(float X[][D], int *y, unsigned int N) {
+                _alphas = (float *) malloc(sizeof(float) * N);
+
+                for (unsigned int i = 0; i < N; i++)
+                    _alphas[i] = 0;
+
+                unsigned int iter = 0;
+                unsigned int passes = 0;
+                
+                if (_cache != NULL) {
+                    for (unsigned int i = 0; i < N; i++) {
+                        for (unsigned int j = 0; j < N; j++) {
+                            _cache[i * N + j] = _kernel(X[i], X[j], D);
+                        }        }         }
+
+                while(passes < _params.passes && iter < _params.maxIter) {
+                    float alphaChanged = 0;
+                    for (unsigned int i = 0; i < N; i++) {
+                        float Ei = margin(X, y, X[i], N) - y[i];
+                        if ((y[i] * Ei < -_params.tol && _alphas[i] < _params.C) || (y[i] * Ei > _params.tol && _alphas[i] > 0)) {
+                            unsigned int j = i;
+
+                            while (j == i)
+                                j = random(0, N);
+                            float Ej = margin(X, y, X[j], N) - y[j];  float ai = _alphas[i]; float aj = _alphas[j];  float L = 0;  float H = 0;
+
+                            if (y[i] == y[j]) {
+                                L = max(0, ai + aj - _params.C);
+                                H = min(_params.C, ai + aj); } 
+                                else {
+                                L = max(0, aj - ai);
+                                H = min(_params.C, _params.C + aj - ai);     }
+                            if (abs(L - H) < 1e-4)
+                                continue;
+                            double eta;
+                            if (_cache != NULL)
+                                eta = _cache[i * N + j] - _cache[i * N + i] - _cache[j * N + j];
+                            else {
+                                eta = _kernel(X[i], X[j], D) - _kernel(X[i], X[i], D) - _kernel(X[j], X[j], D);    }
+                            if (eta >= 0)
+                                continue;
+                            eta *= 2;
+                            float newaj = aj - y[j] * (Ei - Ej) / eta;
+                            if (newaj > H)
+                                newaj = H;
+                            if (newaj < L)
+                                newaj = L;
+                            if (abs(aj - newaj) < 1e-4)
+                                continue;
+                            float newai = ai + y[i] * y[j] * (aj - newaj);
+                            _alphas[i] = newai;
+                            _alphas[j] = newaj;
+                            float b1 = _b - Ei - y[i] * (newai - ai) * _kernel(X[i], X[i], D)
+                                       - y[j] * (newaj - aj) * _kernel(X[i], X[j], D);
+                            float b2 = _b - Ej - y[i] * (newai - ai) * _kernel(X[i], X[j], D)
+                                       - y[j] * (newaj - aj) * _kernel(X[j], X[j], D);
+                            _b = 0.5 * (b1 + b2);
+                           if (newai > 0 && newai < _params.C)
+                                _b = b1;
+                            if (newaj > 0 && newaj < _params.C)
+                                _b = b2;
+                            alphaChanged++;  } } 
+                    iter++;
+                    if(alphaChanged == 0)
+                        passes++;
+                    else passes= 0;      }
+               _y = y;
+                _numSamples = N;  }     
+                
+            int predict(float X_train[][D], float x[D]) {
+                return margin(X_train, _y, x, _numSamples, true) > 0 ? 1 : -1;      }
+            float score(float X_train[][D], float X_test[][D], int y_test[], unsigned int testSize) {
+                unsigned int correct = 0;
+                for (unsigned int i = 0; i < testSize; i++)
+                    if (predict(X_train, X_test[i]) == y_test[i])
+                        correct += 1;
+                return 1.0 * correct / testSize;      }
+
+        protected:
+            kernelFunction _kernel;
+            struct {
+                float C;  float tol;   float alphaTol;  unsigned int maxIter;  unsigned int passes;
+            } _params;
+            float _b = 0;
+            unsigned int _numSamples;
+            int *_y;
+            float *_alphas;
+            float *_cache;
+            float margin(float X[][D], int *y, float x[D], unsigned int N, bool skipSmallAlfas = false) {
+                float sum = _b;
+                for(unsigned int i = 0; i < N; i++)
+                    if ((!skipSmallAlfas && _alphas[i] != 0) || (skipSmallAlfas && _alphas[i] > _params.alphaTol))
+                        sum += _alphas[i] * y[i] * _kernel(x, X[i], D);
+                return sum;  }   };  }  }
+
+using namespace Edge2Train::SVM_train;
+
+float X_train[TOTAL_SAMPLES][Feature_dimension]; float X_test[TOTAL_SAMPLES][Feature_dimension];
+int y_train[TOTAL_SAMPLES]; int y_test[TOTAL_SAMPLES];
+float cache[TOTAL_SAMPLES * TOTAL_SAMPLES];
+SVMSMO<Feature_dimension> classifier(linearKernel);
+int temp, temp1 = 0;
+
+void setup() {
+    Serial.begin(115200);
+    delay(3000);
+    classifier.setC(10);
+    classifier.setTol(1e-5);
+    classifier.setMaxIter(10000);}
+
+void loop() {
+    int tp = 0; int tn = 0; int fp = 0; int fn = 0;
+
+    int positive_sample_inputs = readSerialNumber("Enter the number of positive samples that need to be used for training? ", POSITIVE_SAMPLES);
+    if (positive_sample_inputs > POSITIVE_SAMPLES - 1) {
+        positive_sample_inputs = POSITIVE_SAMPLES - 1;    }
+    int negative_sample_inputs = readSerialNumber("Enter the number of negative samples that need to be used for training? ", NEGATIVE_SAMPLES);
+    if (negative_sample_inputs > NEGATIVE_SAMPLES - 1) {
+        negative_sample_inputs = NEGATIVE_SAMPLES - 1;    }
+
+    loadDataset(positive_sample_inputs, negative_sample_inputs);
+    time_t start = millis();
+    classifier.fit(X_train, y_train, positive_sample_inputs + negative_sample_inputs);
+    Serial.print("It took ");
+    temp1 = millis() - start;
+    Serial.print(temp1);
+    Serial.print("ms to train on ");
+    Serial.print(positive_sample_inputs + negative_sample_inputs);
+    Serial.println(" samples");
+
+    start = millis();
+
+    for (int i = 0; i < TOTAL_SAMPLES - positive_sample_inputs - negative_sample_inputs; i++) {
+        int y_pred = classifier.predict(X_train, X_test[i]);
+        int y_true = y_test[i];
+        if (y_pred == y_true && y_pred ==  1) tp += 1;
+        if (y_pred == y_true && y_pred == -1) tn += 1;
+        if (y_pred != y_true && y_pred ==  1) fp += 1;
+        if (y_pred != y_true && y_pred == -1) fn += 1;    }
+
+    Serial.print("It took ");
+    temp = millis() - start;
+    Serial.print(temp);
+    Serial.print("ms to test on ");
+    Serial.print(TOTAL_SAMPLES - positive_sample_inputs - negative_sample_inputs);
+    Serial.println(" samples");}
+
+int readSerialNumber(String prompt, int maxAllowed) {
+    Serial.print(prompt);
+    Serial.print(" (");
+    Serial.print(maxAllowed);
+    Serial.print(" max) ");
+    while (!Serial.available()) delay(1);
+    int n = Serial.readStringUntil('\n').toInt();
+    Serial.println(n);
+    return n; }
+
+void loadDataset(int positive_sample_inputs, int negative_sample_inputs) {
+    int positiveTestSamples = POSITIVE_SAMPLES - positive_sample_inputs;
+
+    for (int i = 0; i < positive_sample_inputs; i++) {
+        memcpy(X_train[i], X_positive[i], Feature_dimension);
+        y_train[i] = 1;    }
+
+    for (int i = 0; i < negative_sample_inputs; i++) {
+        memcpy(X_train[i + positive_sample_inputs], X_negative[i], Feature_dimension);
+        y_train[i + positive_sample_inputs] = -1;    }
+
+    for (int i = 0; i < positiveTestSamples; i++) {
+        memcpy(X_test[i], X_positive[i + positive_sample_inputs], Feature_dimension);
+        y_test[i] = 1;    }
+
+    for (int i = 0; i < NEGATIVE_SAMPLES - negative_sample_inputs; i++) {
+        memcpy(X_test[i + positiveTestSamples], X_negative[i + negative_sample_inputs], Feature_dimension);
+        y_test[i + positiveTestSamples] = -1;    }}
diff --git a/E2T/breast_cancer.h b/E2T/breast_cancer.h
new file mode 100644
index 0000000..a759ae3
--- /dev/null
+++ b/E2T/breast_cancer.h
@@ -0,0 +1,89 @@
+#define Feature_dimension 30
+#define POSITIVE_SAMPLES 40
+#define NEGATIVE_SAMPLES 40
+
+float X_positive[POSITIVE_SAMPLES][Feature_dimension] = {
+        {0.007925415, 0.004572863, 0.054098996, 0.440986119, 0.000052161, 0.000122295, 0.000132208, 0.000064804, 0.000106568, 0.000034675, 0.000482397, 0.000398826, 0.003783846, 0.067579691, 0.000002819, 0.000021604, 0.000023671, 0.000006991, 0.000013230, 0.000002728, 0.011181047, 0.007634655, 0.081324713, 0.889461512, 0.000071456, 0.000293227, 0.000313624, 0.000116921, 0.000202695, 0.000052381},
+        {0.008665756, 0.007486168, 0.055988282, 0.558618978, 0.000035699, 0.000033130, 0.000036609, 0.000029561, 0.000076336, 0.000023874, 0.000228966, 0.000309178, 0.001431514, 0.031208517, 0.000002201, 0.000005510, 0.000007836, 0.000005645, 0.000005852, 0.000001488, 0.010527819, 0.009862195, 0.066899467, 0.824026185, 0.000052155, 0.000078611, 0.000101782, 0.000078358, 0.000115852, 0.000037502},
+        {0.009366683, 0.010108787, 0.061841988, 0.572276245, 0.000052138, 0.000076066, 0.000093905, 0.000060843, 0.000098424, 0.000028538, 0.000354688, 0.000374334, 0.002181119, 0.044730786, 0.000002926, 0.000019057, 0.000018229, 0.000009790, 0.000010703, 0.000002174, 0.011212428, 0.012144815, 0.072545409, 0.812984292, 0.000068692, 0.000201938, 0.000214259, 0.000115597, 0.000171873, 0.000041662},
+        {0.016324655, 0.029132790, 0.110899010, 0.551921989, 0.000203701, 0.000405829, 0.000345076, 0.000150381, 0.000371236, 0.000139288, 0.000708450, 0.001652478, 0.004924556, 0.038924724, 0.000013023, 0.000106611, 0.000080923, 0.000026688, 0.000085240, 0.000013163, 0.021313538, 0.037881204, 0.141332626, 0.811515444, 0.000299905, 0.001238358, 0.000981909, 0.000368091, 0.000948888, 0.000247300},
+        {0.009883338, 0.006985070, 0.065807736, 0.631773751, 0.000048857, 0.000064687, 0.000096447, 0.000050805, 0.000088117, 0.000028656, 0.000368835, 0.000380574, 0.002648871, 0.046002092, 0.000005597, 0.000011988, 0.000027706, 0.000009182, 0.000008554, 0.000002492, 0.010979322, 0.008120022, 0.074137213, 0.767188634, 0.000066928, 0.000099856, 0.000194842, 0.000079154, 0.000115151, 0.000037400},
+        {0.013945213, 0.017585529, 0.092486442, 0.534398465, 0.000143148, 0.000190417, 0.000176751, 0.000090605, 0.000233764, 0.000085273, 0.000374673, 0.000997111, 0.002483256, 0.030455448, 0.000008412, 0.000037467, 0.000041130, 0.000012736, 0.000024250, 0.000005692, 0.017327907, 0.026602313, 0.115818070, 0.830664225, 0.000200609, 0.000587939, 0.000599812, 0.000195009, 0.000446359, 0.000139340},
+        {0.009483176, 0.010382130, 0.062147282, 0.540411148, 0.000049172, 0.000056639, 0.000058562, 0.000038452, 0.000093221, 0.000029837, 0.000232117, 0.000401775, 0.001652411, 0.028013043, 0.000002242, 0.000007181, 0.000011712, 0.000005399, 0.000007114, 0.000001132, 0.011889045, 0.014372858, 0.079606719, 0.834519522, 0.000074930, 0.000133856, 0.000196627, 0.000100392, 0.000159161, 0.000043482},
+        {0.012711538, 0.019313008, 0.083630981, 0.535813127, 0.000110241, 0.000152520, 0.000086839, 0.000055491, 0.000203607, 0.000069084, 0.000541005, 0.001276717, 0.003575178, 0.047248723, 0.000008164, 0.000028084, 0.000023068, 0.000013425, 0.000013778, 0.000005018, 0.015817567, 0.026090641, 0.102545305, 0.831673949, 0.000153354, 0.000341385, 0.000248297, 0.000144268, 0.000296324, 0.000106718},
+        {0.014200063, 0.023834259, 0.095577344, 0.567784039, 0.000139051, 0.000211035, 0.000203061, 0.000102164, 0.000256693, 0.000080711, 0.000334575, 0.001094497, 0.002628104, 0.026565040, 0.000006260, 0.000038253, 0.000038810, 0.000013392, 0.000023408, 0.000004095, 0.016919921, 0.033566763, 0.116003588, 0.807546634, 0.000186021, 0.000589958, 0.000588756, 0.000225016, 0.000478214, 0.000117096},
+        {0.014365026, 0.027715507, 0.096808285, 0.548660982, 0.000136733, 0.000276233, 0.000262052, 0.000098492, 0.000234037, 0.000095033, 0.000343100, 0.001843473, 0.002350745, 0.027600218, 0.000008242, 0.000083204, 0.000089268, 0.000016509, 0.000020625, 0.000011621, 0.017397130, 0.046899619, 0.112579838, 0.820166889, 0.000213631, 0.001219759, 0.001273945, 0.000254789, 0.000503352, 0.000239225},
+        {0.011359644, 0.016479284, 0.072823688, 0.565713129, 0.000058188, 0.000047289, 0.000023393, 0.000023563, 0.000108349, 0.000040397, 0.000269100, 0.000841692, 0.001748619, 0.028725293, 0.000002857, 0.000006573, 0.000007807, 0.000005383, 0.000010353, 0.000002157, 0.013607464, 0.024024017, 0.087785517, 0.815455125, 0.000083744, 0.000109980, 0.000103456, 0.000070732, 0.000209040, 0.000059932},
+        {0.010334705, 0.011716596, 0.067850156, 0.511495870, 0.000063593, 0.000084616, 0.000065191, 0.000043264, 0.000120637, 0.000039832, 0.000331261, 0.000645035, 0.002334150, 0.035470699, 0.000003780, 0.000026596, 0.000018279, 0.000008396, 0.000013151, 0.000002714, 0.013373554, 0.017866335, 0.089397165, 0.850746652, 0.000091427, 0.000367347, 0.000259677, 0.000118541, 0.000248347, 0.000068636},
+        {0.010902163, 0.014103998, 0.075297153, 0.638660895, 0.000055392, 0.000139789, 0.000117439, 0.000063582, 0.000136320, 0.000044359, 0.000543402, 0.002029156, 0.006295615, 0.066084057, 0.000001785, 0.000047186, 0.000050558, 0.000023260, 0.000025501, 0.000007302, 0.011920153, 0.017027166, 0.086273248, 0.757521204, 0.000058975, 0.000221967, 0.000206953, 0.000100491, 0.000180622, 0.000058179},
+        {0.013360475, 0.020188226, 0.087412068, 0.659763028, 0.000070815, 0.000084462, 0.000083771, 0.000045215, 0.000155690, 0.000044996, 0.000339955, 0.000908681, 0.002447032, 0.030834460, 0.000008235, 0.000026350, 0.000042577, 0.000016791, 0.000025128, 0.000002530, 0.014194978, 0.023315504, 0.094408406, 0.738830067, 0.000095336, 0.000162180, 0.000195729, 0.000094324, 0.000236780, 0.000052995},
+        {0.014943869, 0.024608949, 0.101875171, 0.629427471, 0.000123099, 0.000249572, 0.000231614, 0.000087345, 0.000225192, 0.000083612, 0.000230852, 0.001272351, 0.002243213, 0.020908355, 0.000006997, 0.000064608, 0.000059873, 0.000017719, 0.000021344, 0.000008809, 0.016358801, 0.034840002, 0.118419002, 0.759383618, 0.000179696, 0.000840797, 0.000755683, 0.000240321, 0.000391392, 0.000155751},
+        {0.012504203, 0.023684027, 0.083186488, 0.566559067, 0.000097952, 0.000137168, 0.000140952, 0.000063329, 0.000198055, 0.000060861, 0.000318195, 0.000888366, 0.002475901, 0.027992559, 0.000004822, 0.000036463, 0.000040772, 0.000009374, 0.000015970, 0.000004701, 0.015015363, 0.031931297, 0.106724317, 0.811139211, 0.000144306, 0.000565613, 0.000604226, 0.000147230, 0.000362742, 0.000115324},
+        {0.010966838, 0.015038314, 0.070776446, 0.511362435, 0.000073712, 0.000053788, 0.000055245, 0.000039288, 0.000118484, 0.000044241, 0.000353135, 0.000926354, 0.002386856, 0.033916515, 0.000004272, 0.000008681, 0.000014926, 0.000008285, 0.000010534, 0.000001558, 0.014246430, 0.023069207, 0.092187180, 0.850154056, 0.000109370, 0.000139775, 0.000217693, 0.000120202, 0.000226284, 0.000061378},
+        {0.010405852, 0.013341166, 0.069737915, 0.515325129, 0.000075480, 0.000130444, 0.000111090, 0.000066319, 0.000139605, 0.000047455, 0.000367205, 0.000692218, 0.002486308, 0.034952824, 0.000004533, 0.000016135, 0.000020567, 0.000008367, 0.000010896, 0.000002672, 0.013521801, 0.020308507, 0.088252977, 0.848338189, 0.000115413, 0.000273081, 0.000308627, 0.000133734, 0.000239083, 0.000073673},
+        {0.007279816, 0.008139724, 0.047772647, 0.463027195, 0.000036127, 0.000037740, 0.000054351, 0.000034903, 0.000058136, 0.000019826, 0.000278625, 0.000373729, 0.002155281, 0.041304966, 0.000002386, 0.000006956, 0.000012461, 0.000005589, 0.000004983, 0.000000734, 0.010039606, 0.011347841, 0.068645619, 0.881221598, 0.000055563, 0.000115757, 0.000197411, 0.000087755, 0.000101719, 0.000027984},
+        {0.014724007, 0.015615712, 0.095107951, 0.615820180, 0.000106341, 0.000088398, 0.000072467, 0.000051991, 0.000204983, 0.000062702, 0.000293501, 0.000857559, 0.002237962, 0.025620207, 0.000009202, 0.000015877, 0.000025957, 0.000014300, 0.000021531, 0.000002501, 0.016431296, 0.020944193, 0.108418280, 0.773390980, 0.000156592, 0.000192804, 0.000259899, 0.000140063, 0.000323732, 0.000078938},
+        {0.015795051, 0.018970967, 0.103404448, 0.627937790, 0.000129814, 0.000153362, 0.000055162, 0.000037556, 0.000237530, 0.000082248, 0.000223642, 0.000902902, 0.001670073, 0.017715091, 0.000004947, 0.000022920, 0.000020505, 0.000007837, 0.000020263, 0.000002928, 0.017509804, 0.024743164, 0.116035658, 0.761374570, 0.000158434, 0.000335222, 0.000228231, 0.000087948, 0.000384491, 0.000098816},
+        {0.022222091, 0.029086996, 0.141085958, 0.640428305, 0.000239430, 0.000151795, 0.000069117, 0.000048541, 0.000424380, 0.000161452, 0.000648378, 0.002283937, 0.004463591, 0.036709472, 0.000022461, 0.000033483, 0.000046413, 0.000033226, 0.000047395, 0.000006940, 0.023919611, 0.036615945, 0.152285854, 0.736293805, 0.000309575, 0.000268423, 0.000207327, 0.000145599, 0.000572855, 0.000181747},
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+        {0.007102607, 0.007733652, 0.046052821, 0.471269390, 0.000031646, 0.000034305, 0.000036822, 0.000028974, 0.000059379, 0.000017716, 0.000232177, 0.000378291, 0.001444353, 0.031548867, 0.000001587, 0.000004226, 0.000005757, 0.000003484, 0.000003635, 0.000000667, 0.009791259, 0.011946209, 0.063104448, 0.877756022, 0.000047026, 0.000087272, 0.000105901, 0.000067434, 0.000094724, 0.000025262},
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+        {0.011795821, 0.018630224, 0.077639218, 0.438955981, 0.000110486, 0.000151034, 0.000121346, 0.000051627, 0.000229241, 0.000077699, 0.000480700, 0.001026157, 0.003462034, 0.040847016, 0.000005530, 0.000034013, 0.000041893, 0.000010401, 0.000022645, 0.000005646, 0.016757239, 0.028015075, 0.118954480, 0.885383976, 0.000163089, 0.000575345, 0.000693004, 0.000154023, 0.000474324, 0.000139677},
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+        {0.014352577, 0.022167704, 0.094122238, 0.595397688, 0.000108177, 0.000133624, 0.000113181, 0.000057911, 0.000183134, 0.000068345, 0.000226788, 0.000629682, 0.001645010, 0.019718822, 0.000005714, 0.000023839, 0.000032464, 0.000013437, 0.000014661, 0.000003393, 0.016535276, 0.027704306, 0.114245658, 0.788326981, 0.000171422, 0.000449849, 0.000535560, 0.000240416, 0.000298870, 0.000114033}
+};
+
+float X_negative[NEGATIVE_SAMPLES][Feature_dimension] = {
+        {0.013731964,0.022048794,0.088052134,0.575230348,0.000083393,0.000061620,0.000031776,0.000020751,0.000182275,0.000057084,0.000243681,0.000844454,0.001606149,0.020975984,0.000003351,0.000011259,0.000014202,0.000007030,0.000014100,0.000001314,0.016276056,0.030907137,0.104726662,0.805015970,0.000111777,0.000208738,0.000213029,0.000113616,0.000305904,0.000073012},
+        {0.016957465,0.033063184,0.111346275,0.574695446,0.000190017,0.000188623,0.000161677,0.000087792,0.000293465,0.000106391,0.000366405,0.002211439,0.002821598,0.026280199,0.000012488,0.000027318,0.000040187,0.000016059,0.000021015,0.000004708,0.019884370,0.054728475,0.135071239,0.795994231,0.000295633,0.000417820,0.000623013,0.000220524,0.000459013,0.000148761},
+        {0.009527505,0.012395249,0.064099576,0.551566107,0.000045369,0.000109414,0.000105267,0.000049766,0.000115409,0.000031690,0.000490164,0.000832345,0.004411530,0.052408772,0.000003271,0.000050260,0.000048577,0.000013180,0.000026644,0.000003820,0.012035532,0.016571964,0.088630278,0.824851126,0.000062301,0.000371907,0.000361815,0.000124552,0.000233316,0.000051859},
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+        {0.014872465,0.024585320,0.095816598,0.618491672,0.000096489,0.000084869,0.000052379,0.000037308,0.000199439,0.000063040,0.000257760,0.001491656,0.001912804,0.022270110,0.000004912,0.000015236,0.000023097,0.000013053,0.000018092,0.000002156,0.016702583,0.035080936,0.109145589,0.770413512,0.000128108,0.000188634,0.000251586,0.000141338,0.000316522,0.000076259},
+        {0.017253243,0.031689631,0.109622649,0.627777456,0.000126715,0.000072857,0.000024310,0.000016358,0.000219333,0.000086384,0.000595941,0.001775206,0.003865842,0.041768694,0.000008593,0.000014316,0.000017136,0.000010923,0.000035299,0.000002595,0.019043121,0.037734135,0.121740999,0.757763627,0.000159182,0.000126392,0.000081029,0.000054503,0.000356948,0.000096287},
+        {0.015479346,0.018543439,0.098970216,0.648906888,0.000087213,0.000068761,0.000021074,0.000019553,0.000153545,0.000067558,0.000212444,0.001047920,0.001644397,0.016512059,0.000005081,0.000013357,0.000012245,0.000009029,0.000015037,0.000002895,0.016648241,0.026317158,0.109036330,0.745255602,0.000123585,0.000179533,0.000119159,0.000097438,0.000266236,0.000091072},
+        {0.017170061,0.026229642,0.108873145,0.629281313,0.000118796,0.000068321,0.000028358,0.000019399,0.000268624,0.000087864,0.000326864,0.000910128,0.002185803,0.025122360,0.000010368,0.000012051,0.000018853,0.000011504,0.000028703,0.000003789,0.018838175,0.030673150,0.120319853,0.758128675,0.000164510,0.000128071,0.000132342,0.000090538,0.000400491,0.000106529},
+        {0.010780625,0.011064637,0.071180526,0.611217652,0.000067926,0.000087866,0.000104848,0.000062719,0.000123782,0.000037336,0.000493293,0.000942565,0.002885681,0.058465069,0.000002307,0.000017520,0.000016668,0.000005457,0.000015822,0.000003033,0.012188852,0.014277524,0.079937565,0.781624897,0.000075737,0.000135912,0.000155201,0.000078399,0.000178750,0.000047258},
+        {0.011955390,0.017434283,0.077005377,0.564357726,0.000071701,0.000056064,0.000041591,0.000026397,0.000127946,0.000045003,0.000245838,0.000660238,0.001660295,0.023681172,0.000003701,0.000008155,0.000012692,0.000007301,0.000008670,0.000001290,0.014330632,0.025090483,0.093188699,0.815500081,0.000109974,0.000162862,0.000214722,0.000121137,0.000211792,0.000062334},
+        {0.017420546,0.028353754,0.110904763,0.618489886,0.000144022,0.000082763,0.000045910,0.000034448,0.000290342,0.000089326,0.000491314,0.001450351,0.003301133,0.035491339,0.000012594,0.000013189,0.000020400,0.000013111,0.000048663,0.000003608,0.019416651,0.033979139,0.123713099,0.765475746,0.000188874,0.000131864,0.000137247,0.000095511,0.000499933,0.000106398},
+        {0.007851921,0.007590326,0.051297034,0.470869989,0.000043040,0.000051788,0.000054076,0.000036762,0.000078356,0.000024365,0.000297359,0.000487628,0.001977082,0.041895984,0.000002640,0.000009426,0.000012037,0.000006286,0.000007570,0.000001066,0.010684498,0.011501980,0.069526897,0.876750110,0.000066380,0.000143509,0.000158551,0.000085468,0.000144572,0.000033901},
+        {0.012306262,0.018062012,0.079936323,0.549556990,0.000095120,0.000114195,0.000108171,0.000067956,0.000169577,0.000056537,0.000353544,0.000962080,0.002288078,0.033538955,0.000003061,0.000023885,0.000021517,0.000010641,0.000015201,0.000003437,0.014949891,0.025683361,0.096793642,0.824460973,0.000114446,0.000358898,0.000300085,0.000153431,0.000309372,0.000091523},
+        {0.015770535,0.023335558,0.099831715,0.637105439,0.000097403,0.000045789,0.000000836,0.000005036,0.000219821,0.000066478,0.000488222,0.001467083,0.003135980,0.039831175,0.000009053,0.000010384,0.000000836,0.000005036,0.000026465,0.000003613,0.017196530,0.026888460,0.109052342,0.754206192,0.000123385,0.000074816,0.000002230,0.000013426,0.000294746,0.000076001},
+        {0.023620016,0.032313760,0.148933819,0.615304423,0.000267281,0.000144494,0.000056487,0.000021375,0.000461273,0.000196980,0.000427287,0.001588554,0.002866853,0.022808746,0.000027709,0.000028915,0.000054295,0.000015738,0.000057282,0.000007641,0.026056566,0.042207965,0.164172539,0.753439581,0.000474977,0.000339582,0.000320123,0.000121115,0.000882530,0.000247383},
+        {0.020581917,0.030113956,0.130635474,0.631219278,0.000229497,0.000163137,0.000021938,0.000026107,0.000555125,0.000140856,0.001043870,0.002916278,0.006702784,0.070063518,0.000015207,0.000022241,0.000015512,0.000016581,0.000084655,0.000012048,0.022302137,0.035315089,0.141381785,0.745968022,0.000258033,0.000199667,0.000043876,0.000052193,0.000719861,0.000162308},
+        {0.023599037,0.057584065,0.150025978,0.608777202,0.000341168,0.000246009,0.000082341,0.000025413,0.000501733,0.000185460,0.000983156,0.005673320,0.006842568,0.050475260,0.000032744,0.000086788,0.000082341,0.000025413,0.000092140,0.000008366,0.026253174,0.074217022,0.170336849,0.751776716,0.000449858,0.000466052,0.000247052,0.000076248,0.000815729,0.000211672},
+        {0.012189493,0.018947178,0.082477962,0.552333749,0.000089732,0.000171765,0.000182629,0.000074018,0.000166718,0.000062376,0.000601862,0.001084651,0.004596081,0.051991397,0.000008047,0.000060357,0.000059014,0.000015808,0.000014542,0.000005229,0.015114972,0.025242944,0.101878503,0.820759226,0.000140286,0.000534370,0.000592110,0.000152689,0.000243277,0.000096832},
+        {0.022031819,0.033289111,0.142187255,0.626632683,0.000185444,0.000210183,0.000143820,0.000052359,0.000562264,0.000167238,0.000984262,0.005440104,0.006263925,0.056490612,0.000020987,0.000094583,0.000103566,0.000037468,0.000100684,0.000013983,0.024042136,0.046186840,0.157534832,0.744801820,0.000236242,0.000403024,0.000335533,0.000122180,0.000788275,0.000203914},
+        {0.012310701,0.023145672,0.080281694,0.484466870,0.000108932,0.000122524,0.000109515,0.000066728,0.000184952,0.000063981,0.000413108,0.001143691,0.002841753,0.035398120,0.000007554,0.000025709,0.000028864,0.000012524,0.000015874,0.000003496,0.016592262,0.032495203,0.108543880,0.862428699,0.000179709,0.000394273,0.000390680,0.000166602,0.000328447,0.000100097},
+        {0.012823065,0.020770242,0.084503826,0.579814242,0.000101682,0.000128317,0.000109924,0.000078335,0.000169441,0.000057730,0.000310339,0.001111390,0.002125610,0.030574074,0.000005815,0.000020041,0.000020085,0.000010272,0.000016484,0.000002797,0.015018082,0.028969022,0.099426473,0.802612831,0.000142980,0.000296371,0.000262361,0.000140030,0.000288129,0.000077311},
+        {0.021607044,0.047962387,0.137221280,0.614996056,0.000238328,0.000177445,0.000049583,0.000034334,0.000391963,0.000157493,0.000536695,0.004590783,0.003789508,0.032416273,0.000024015,0.000040064,0.000039128,0.000021306,0.000052026,0.000009672,0.023764324,0.072046309,0.153018506,0.754933911,0.000353383,0.000379864,0.000214861,0.000148772,0.000657000,0.000210272},
+        {0.018206267,0.030649631,0.115580017,0.634402295,0.000131018,0.000075674,0.000059706,0.000035897,0.000244038,0.000091225,0.000438979,0.001517833,0.002947451,0.029216954,0.000014942,0.000014835,0.000033209,0.000014431,0.000035141,0.000003455,0.019848212,0.038376429,0.125560464,0.751270112,0.000207658,0.000147260,0.000232448,0.000112055,0.000386340,0.000106903},
+        {0.021446155,0.041163126,0.139641095,0.595000752,0.000253202,0.000335623,0.000743454,0.000103917,0.000501416,0.000191113,0.000777658,0.002836051,0.004477351,0.041970712,0.000022681,0.000204414,0.000721602,0.000078906,0.000099689,0.000022705,0.024488853,0.053799469,0.155579039,0.771244488,0.000352012,0.001036798,0.002973816,0.000415669,0.001004257,0.000279092},
+        {0.016843125,0.021732640,0.107239836,0.662259032,0.000129566,0.000068980,0.000048144,0.000037745,0.000209551,0.000074502,0.000312085,0.001150815,0.001938673,0.024157628,0.000010493,0.000007396,0.000020889,0.000011416,0.000029706,0.000002512,0.017739317,0.026042266,0.112906928,0.731318481,0.000170804,0.000093059,0.000136933,0.000077521,0.000314062,0.000084479},
+        {0.008633160,0.009713444,0.056338887,0.515072353,0.000041064,0.000046903,0.000049228,0.000036242,0.000072110,0.000024892,0.000359548,0.000363513,0.002500608,0.043781150,0.000002026,0.000007530,0.000010342,0.000006245,0.000006318,0.000000774,0.011331592,0.012115596,0.075619914,0.850553107,0.000054379,0.000106479,0.000122478,0.000081546,0.000116279,0.000030034},
+        {0.023019305,0.037916587,0.152262031,0.631943113,0.000253373,0.000396518,0.000222889,0.000074383,0.000492605,0.000232576,0.001362822,0.002207139,0.008204901,0.065887839,0.000044573,0.000242625,0.000146875,0.000045738,0.000065810,0.000056797,0.025207796,0.040584215,0.162026070,0.736576317,0.000312605,0.000630907,0.000371396,0.000123954,0.000583770,0.000280748},
+        {0.008902247,0.012690878,0.059106779,0.481031881,0.000055432,0.000094716,0.000087573,0.000041116,0.000099736,0.000033575,0.000305730,0.000538793,0.001917606,0.035955762,0.000003012,0.000029067,0.000022007,0.000005833,0.000007903,0.000003260,0.012069791,0.017504302,0.078463991,0.870039371,0.000082035,0.000382693,0.000339838,0.000098287,0.000171472,0.000069303},
+        {0.013642589,0.015609889,0.089398498,0.577437387,0.000099551,0.000126540,0.000077002,0.000050112,0.000164304,0.000064911,0.000275521,0.000613422,0.001934677,0.023083655,0.000004663,0.000020414,0.000017389,0.000009101,0.000012061,0.000003094,0.016380992,0.020622058,0.109041849,0.803133254,0.000139491,0.000350160,0.000274730,0.000136722,0.000255947,0.000101825},
+        {0.015747627,0.021133289,0.101304188,0.602401089,0.000117333,0.000087360,0.000043136,0.000029065,0.000220032,0.000075655,0.000320453,0.001311236,0.002225903,0.025175735,0.000006210,0.000023270,0.000023359,0.000010189,0.000017730,0.000002947,0.018050285,0.029691504,0.114761949,0.781752613,0.000150440,0.000235767,0.000217857,0.000110783,0.000334909,0.000097338},
+        {0.010835045,0.013248826,0.070188438,0.551326470,0.000061814,0.000056798,0.000065867,0.000044756,0.000121228,0.000036348,0.000503927,0.000685028,0.003390756,0.053433561,0.000007295,0.000014854,0.000023598,0.000012197,0.000010451,0.000001313,0.013329735,0.016559347,0.086842178,0.824596151,0.000101136,0.000137882,0.000190743,0.000102485,0.000178674,0.000043064},
+        {0.016204567,0.013102584,0.105287766,0.669740857,0.000154620,0.000125397,0.000082364,0.000078520,0.000287802,0.000079538,0.000491167,0.001214444,0.003176239,0.039104147,0.000016049,0.000034002,0.000013917,0.000009868,0.000030804,0.000007382,0.016863289,0.014958983,0.109419751,0.725193292,0.000173783,0.000165160,0.000102270,0.000088712,0.000324570,0.000086125},
+        {0.009433891,0.008440849,0.062822917,0.525789136,0.000055663,0.000112161,0.000088015,0.000056447,0.000112475,0.000034877,0.000512514,0.000287721,0.003298464,0.070453654,0.000004150,0.000030518,0.000024345,0.000010819,0.000013542,0.000003687,0.011702206,0.009883372,0.078450248,0.841471678,0.000077248,0.000294463,0.000197877,0.000109862,0.000196047,0.000057910},
+        {0.009788773,0.011627199,0.069704986,0.603915848,0.000062380,0.000167544,0.000182096,0.000077806,0.000140962,0.000039495,0.000451942,0.000914363,0.004195396,0.056462494,0.000005035,0.000033155,0.000052921,0.000012578,0.000038297,0.000002904,0.011336155,0.015386515,0.082607927,0.787273431,0.000079503,0.000298999,0.000372585,0.000121656,0.000263880,0.000048333},
+        {0.015824321,0.022149127,0.102365882,0.623005722,0.000122239,0.000117464,0.000047854,0.000028486,0.000211401,0.000073794,0.000326700,0.001347405,0.002187842,0.025040819,0.000006513,0.000020439,0.000025484,0.000010064,0.000021509,0.000003504,0.017522420,0.030541185,0.113058988,0.765498438,0.000158612,0.000263452,0.000213001,0.000097530,0.000341958,0.000097432}
+};
diff --git a/E2T/digits.h b/E2T/digits.h
new file mode 100644
index 0000000..94c77d8
--- /dev/null
+++ b/E2T/digits.h
@@ -0,0 +1,100 @@
+
+#define POSITIVE_SAMPLES 45
+#define NEGATIVE_SAMPLES 45
+#define Feature_dimension 64
+
+float X_positive[POSITIVE_SAMPLES][Feature_dimension] = { {0.0000000, 0.0000000, 0.0000000, 12.0000000, 13.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 15.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 15.0000000, 16.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 16.0000000, 10.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 13.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 16.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 10.0000000, 16.0000000, 16.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 12.0000000, 14.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 14.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 13.0000000, 16.0000000, 1.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 1.0000000, 4.0000000, 12.0000000, 16.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 16.0000000, 16.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 10.0000000, 16.0000000, 14.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 12.0000000, 15.0000000, 4.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 12.0000000, 16.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 12.0000000, 16.0000000, 16.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 11.0000000, 5.0000000, 15.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 1.0000000, 11.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 14.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 11.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 14.0000000, 6.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 13.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 15.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 16.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 16.0000000, 16.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 7.0000000, 4.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 12.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 16.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 8.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 5.0000000, 6.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 11.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 1.0000000, 14.0000000, 13.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 5.0000000, 11.0000000, 15.0000000, 16.0000000, 16.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 15.0000000, 16.0000000, 16.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 13.0000000, 15.0000000, 2.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 16.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 12.0000000, 16.0000000, 16.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 12.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 9.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 13.0000000, 5.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 1.0000000, 12.0000000, 8.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 16.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 13.0000000, 16.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 11.0000000, 16.0000000, 16.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 12.0000000, 8.0000000, 16.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 13.0000000, 4.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 11.0000000, 16.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 16.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 16.0000000, 16.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 8.0000000, 14.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 1.0000000, 15.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 16.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 16.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 16.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 16.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 11.0000000, 16.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 16.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 14.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 16.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 16.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 15.0000000, 7.0000000, 15.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 15.0000000, 0.0000000, 0.0000000}
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+        , {0.0000000, 0.0000000, 7.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 9.0000000, 16.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 9.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 4.0000000, 14.0000000, 12.0000000, 4.0000000, 1.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 16.0000000, 16.0000000, 16.0000000, 5.0000000}
+
+};
+
+float X_negative[NEGATIVE_SAMPLES][Feature_dimension] = {
+          {0.0000000, 0.0000000, 5.0000000, 13.0000000, 9.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 15.0000000, 10.0000000, 15.0000000, 5.0000000, 0.0000000, 0.0000000, 3.0000000, 15.0000000, 2.0000000, 0.0000000, 11.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 12.0000000, 0.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 8.0000000, 0.0000000, 0.0000000, 9.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 11.0000000, 0.0000000, 1.0000000, 12.0000000, 7.0000000, 0.0000000, 0.0000000, 2.0000000, 14.0000000, 5.0000000, 10.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 13.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 1.0000000, 9.0000000, 15.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 16.0000000, 8.0000000, 14.0000000, 6.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 10.0000000, 0.0000000, 9.0000000, 9.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 4.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 4.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 5.0000000, 1.0000000, 11.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 12.0000000, 10.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 10.0000000, 13.0000000, 3.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 3.0000000, 13.0000000, 11.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 16.0000000, 16.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 9.0000000, 1.0000000, 14.0000000, 2.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 0.0000000, 0.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 1.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 9.0000000, 0.0000000, 13.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 9.0000000, 14.0000000, 9.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 12.0000000, 13.0000000, 4.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 10.0000000, 14.0000000, 11.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 13.0000000, 6.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 2.0000000, 0.0000000, 11.0000000, 7.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 0.0000000, 0.0000000, 10.0000000, 5.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 0.0000000, 0.0000000, 14.0000000, 4.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 0.0000000, 1.0000000, 16.0000000, 1.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 1.0000000, 11.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 16.0000000, 12.0000000, 3.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 6.0000000, 14.0000000, 10.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 15.0000000, 13.0000000, 15.0000000, 3.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 10.0000000, 0.0000000, 13.0000000, 9.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 5.0000000, 0.0000000, 12.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 3.0000000, 0.0000000, 13.0000000, 6.0000000, 0.0000000, 0.0000000, 1.0000000, 15.0000000, 5.0000000, 6.0000000, 13.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 11.0000000, 14.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 11.0000000, 1.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 15.0000000, 13.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 15.0000000, 11.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 6.0000000, 0.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 8.0000000, 0.0000000, 3.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 14.0000000, 3.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 3.0000000, 15.0000000, 1.0000000, 0.0000000, 3.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 11.0000000, 6.0000000, 14.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 12.0000000, 15.0000000, 6.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 1.0000000, 15.0000000, 13.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 14.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 9.0000000, 2.0000000, 13.0000000, 2.0000000, 0.0000000, 0.0000000, 7.0000000, 9.0000000, 1.0000000, 0.0000000, 6.0000000, 6.0000000, 0.0000000, 0.0000000, 5.0000000, 9.0000000, 0.0000000, 0.0000000, 3.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 2.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 9.0000000, 15.0000000, 13.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 14.0000000, 8.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 14.0000000, 15.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 16.0000000, 15.0000000, 1.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 10.0000000, 10.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 0.0000000, 0.0000000, 14.0000000, 6.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 6.0000000, 0.0000000, 12.0000000, 7.0000000, 0.0000000, 0.0000000, 1.0000000, 15.0000000, 13.0000000, 4.0000000, 13.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 16.0000000, 16.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 11.0000000, 13.0000000, 4.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 1.0000000, 13.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 16.0000000, 16.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 13.0000000, 10.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 2.0000000, 2.0000000, 15.0000000, 3.0000000, 0.0000000, 0.0000000, 5.0000000, 15.0000000, 2.0000000, 0.0000000, 12.0000000, 7.0000000, 0.0000000, 0.0000000, 1.0000000, 15.0000000, 6.0000000, 2.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 15.0000000, 13.0000000, 16.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 15.0000000, 14.0000000, 8.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 10.0000000, 12.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 16.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 15.0000000, 3.0000000, 8.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 7.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 13.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 8.0000000, 0.0000000, 10.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 12.0000000, 13.0000000, 12.0000000, 4.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 4.0000000, 14.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 15.0000000, 15.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 13.0000000, 0.0000000, 3.0000000, 15.0000000, 1.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 8.0000000, 6.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 13.0000000, 1.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 2.0000000, 15.0000000, 14.0000000, 12.0000000, 15.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 15.0000000, 8.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 1.0000000, 12.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 9.0000000, 16.0000000, 14.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 14.0000000, 11.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 10.0000000, 0.0000000, 14.0000000, 4.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 0.0000000, 0.0000000, 12.0000000, 4.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 3.0000000, 0.0000000, 11.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 12.0000000, 8.0000000, 14.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 10.0000000, 16.0000000, 12.0000000, 1.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 3.0000000, 12.0000000, 10.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 16.0000000, 16.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 13.0000000, 6.0000000, 16.0000000, 1.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 7.0000000, 0.0000000, 13.0000000, 3.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 4.0000000, 0.0000000, 13.0000000, 7.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 8.0000000, 0.0000000, 14.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 14.0000000, 13.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 13.0000000, 15.0000000, 6.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 12.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 15.0000000, 12.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 5.0000000, 0.0000000, 14.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 15.0000000, 1.0000000, 0.0000000, 9.0000000, 7.0000000, 0.0000000, 0.0000000, 4.0000000, 10.0000000, 0.0000000, 0.0000000, 7.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 0.0000000, 0.0000000, 8.0000000, 10.0000000, 0.0000000, 0.0000000, 2.0000000, 15.0000000, 5.0000000, 10.0000000, 16.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 14.0000000, 12.0000000, 4.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 12.0000000, 9.0000000, 12.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 16.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 9.0000000, 3.0000000, 15.0000000, 2.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 1.0000000, 0.0000000, 16.0000000, 5.0000000, 0.0000000, 0.0000000, 5.0000000, 12.0000000, 0.0000000, 0.0000000, 16.0000000, 5.0000000, 0.0000000, 0.0000000, 3.0000000, 14.0000000, 1.0000000, 4.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 12.0000000, 14.0000000, 14.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 12.0000000, 12.0000000, 2.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 12.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 12.0000000, 16.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 6.0000000, 4.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 6.0000000, 0.0000000, 13.0000000, 5.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 5.0000000, 0.0000000, 7.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 8.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 14.0000000, 14.0000000, 16.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 14.0000000, 15.0000000, 7.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 5.0000000, 14.0000000, 10.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 16.0000000, 16.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 10.0000000, 2.0000000, 16.0000000, 7.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 3.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 1.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 5.0000000, 2.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 15.0000000, 14.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 15.0000000, 13.0000000, 2.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 5.0000000, 16.0000000, 15.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 15.0000000, 9.0000000, 10.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 1.0000000, 0.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 0.0000000, 0.0000000, 11.0000000, 6.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 1.0000000, 0.0000000, 11.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 4.0000000, 3.0000000, 15.0000000, 4.0000000, 0.0000000, 0.0000000, 1.0000000, 13.0000000, 13.0000000, 13.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 13.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 6.0000000, 15.0000000, 15.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 14.0000000, 14.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 15.0000000, 2.0000000, 1.0000000, 14.0000000, 5.0000000, 0.0000000, 0.0000000, 8.0000000, 14.0000000, 2.0000000, 0.0000000, 9.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 4.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 6.0000000, 0.0000000, 11.0000000, 9.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 16.0000000, 14.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 14.0000000, 15.0000000, 10.0000000, 1.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 10.0000000, 15.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 16.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 6.0000000, 6.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 4.0000000, 0.0000000, 15.0000000, 3.0000000, 0.0000000, 0.0000000, 5.0000000, 15.0000000, 5.0000000, 0.0000000, 11.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 11.0000000, 0.0000000, 13.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 16.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 8.0000000, 13.0000000, 8.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 15.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 16.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 9.0000000, 6.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 3.0000000, 1.0000000, 14.0000000, 7.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 4.0000000, 0.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 12.0000000, 6.0000000, 16.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 16.0000000, 16.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 13.0000000, 15.0000000, 8.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 4.0000000, 15.0000000, 11.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 14.0000000, 14.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 15.0000000, 2.0000000, 3.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 0.0000000, 0.0000000, 12.0000000, 7.0000000, 0.0000000, 0.0000000, 7.0000000, 16.0000000, 0.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 6.0000000, 1.0000000, 14.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 16.0000000, 16.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 13.0000000, 14.0000000, 6.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 12.0000000, 12.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 16.0000000, 8.0000000, 15.0000000, 3.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 6.0000000, 0.0000000, 13.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 4.0000000, 0.0000000, 15.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 8.0000000, 12.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 16.0000000, 16.0000000, 15.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 13.0000000, 12.0000000, 3.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 0.0000000, 14.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 9.0000000, 16.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 8.0000000, 11.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 7.0000000, 4.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 4.0000000, 0.0000000, 16.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 10.0000000, 0.0000000, 13.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 16.0000000, 16.0000000, 16.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 10.0000000, 13.0000000, 6.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 4.0000000, 12.0000000, 10.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 13.0000000, 15.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 2.0000000, 1.0000000, 14.0000000, 3.0000000, 0.0000000, 0.0000000, 8.0000000, 13.0000000, 0.0000000, 0.0000000, 10.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 14.0000000, 0.0000000, 0.0000000, 11.0000000, 8.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 14.0000000, 13.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 13.0000000, 5.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 14.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 15.0000000, 15.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 7.0000000, 4.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 4.0000000, 0.0000000, 13.0000000, 7.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 1.0000000, 0.0000000, 10.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 5.0000000, 1.0000000, 12.0000000, 11.0000000, 0.0000000, 0.0000000, 1.0000000, 15.0000000, 14.0000000, 13.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 12.0000000, 13.0000000, 5.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 4.0000000, 11.0000000, 15.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 16.0000000, 11.0000000, 13.0000000, 7.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 12.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 5.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 7.0000000, 9.0000000, 0.0000000, 0.0000000, 9.0000000, 7.0000000, 0.0000000, 0.0000000, 4.0000000, 10.0000000, 0.0000000, 2.0000000, 15.0000000, 2.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 12.0000000, 14.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 14.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 4.0000000, 13.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 16.0000000, 15.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 9.0000000, 6.0000000, 15.0000000, 6.0000000, 0.0000000, 0.0000000, 8.0000000, 14.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 12.0000000, 0.0000000, 0.0000000, 11.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 10.0000000, 12.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0000000, 15.0000000, 11.0000000, 2.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 5.0000000, 15.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 16.0000000, 10.0000000, 13.0000000, 3.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 9.0000000, 0.0000000, 8.0000000, 4.0000000, 0.0000000, 0.0000000, 4.0000000, 13.0000000, 1.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 4.0000000, 0.0000000, 0.0000000, 1.0000000, 14.0000000, 0.0000000, 0.0000000, 11.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 9.0000000, 9.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 14.0000000, 15.0000000, 4.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 12.0000000, 15.0000000, 12.0000000, 1.0000000, 0.0000000, 0.0000000, 1.0000000, 14.0000000, 14.0000000, 14.0000000, 11.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 3.0000000, 0.0000000, 2.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 14.0000000, 0.0000000, 0.0000000, 6.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 12.0000000, 0.0000000, 0.0000000, 9.0000000, 4.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 1.0000000, 1.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 11.0000000, 9.0000000, 11.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 13.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 9.0000000, 16.0000000, 15.0000000, 14.0000000, 1.0000000, 0.0000000, 0.0000000, 1.0000000, 15.0000000, 15.0000000, 5.0000000, 10.0000000, 7.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 1.0000000, 0.0000000, 1.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 13.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 7.0000000, 6.0000000, 0.0000000, 0.0000000, 6.0000000, 6.0000000, 0.0000000, 0.0000000, 5.0000000, 9.0000000, 0.0000000, 0.0000000, 13.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 5.0000000, 12.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 15.0000000, 10.0000000, 1.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 13.0000000, 10.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 15.0000000, 12.0000000, 13.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 4.0000000, 0.0000000, 6.0000000, 4.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 3.0000000, 0.0000000, 1.0000000, 7.0000000, 0.0000000, 0.0000000, 5.0000000, 13.0000000, 5.0000000, 0.0000000, 2.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 12.0000000, 0.0000000, 0.0000000, 3.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 5.0000000, 6.0000000, 13.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 14.0000000, 13.0000000, 8.0000000, 1.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 5.0000000, 13.0000000, 13.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 16.0000000, 10.0000000, 15.0000000, 3.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 2.0000000, 1.0000000, 8.0000000, 4.0000000, 0.0000000, 0.0000000, 4.0000000, 13.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 6.0000000, 7.0000000, 0.0000000, 0.0000000, 5.0000000, 15.0000000, 0.0000000, 0.0000000, 7.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 8.0000000, 5.0000000, 15.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 14.0000000, 15.0000000, 9.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 4.0000000, 13.0000000, 11.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 16.0000000, 13.0000000, 16.0000000, 2.0000000, 0.0000000, 0.0000000, 5.0000000, 16.0000000, 4.0000000, 0.0000000, 5.0000000, 7.0000000, 0.0000000, 0.0000000, 8.0000000, 14.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 6.0000000, 9.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 2.0000000, 14.0000000, 1.0000000, 0.0000000, 8.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 12.0000000, 9.0000000, 15.0000000, 2.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 12.0000000, 5.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 5.0000000, 8.0000000, 11.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 16.0000000, 12.0000000, 12.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 9.0000000, 0.0000000, 9.0000000, 3.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 6.0000000, 0.0000000, 6.0000000, 6.0000000, 0.0000000, 0.0000000, 3.0000000, 11.0000000, 1.0000000, 0.0000000, 5.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 12.0000000, 0.0000000, 0.0000000, 11.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 5.0000000, 12.0000000, 15.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 13.0000000, 2.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 3.0000000, 11.0000000, 16.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 16.0000000, 10.0000000, 14.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 7.0000000, 0.0000000, 11.0000000, 8.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 2.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 12.0000000, 0.0000000, 0.0000000, 9.0000000, 8.0000000, 0.0000000, 0.0000000, 6.0000000, 15.0000000, 1.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 3.0000000, 15.0000000, 10.0000000, 8.0000000, 15.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 5.0000000, 12.0000000, 14.0000000, 9.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 7.0000000, 15.0000000, 9.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 16.0000000, 14.0000000, 15.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 16.0000000, 8.0000000, 4.0000000, 6.0000000, 4.0000000, 0.0000000, 0.0000000, 4.0000000, 15.0000000, 1.0000000, 0.0000000, 6.0000000, 5.0000000, 0.0000000, 0.0000000, 3.0000000, 11.0000000, 0.0000000, 0.0000000, 7.0000000, 5.0000000, 0.0000000, 0.0000000, 3.0000000, 11.0000000, 0.0000000, 1.0000000, 13.0000000, 2.0000000, 0.0000000, 0.0000000, 1.0000000, 13.0000000, 8.0000000, 13.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 15.0000000, 11.0000000, 1.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 3.0000000, 14.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 14.0000000, 16.0000000, 14.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 16.0000000, 11.0000000, 15.0000000, 3.0000000, 0.0000000, 0.0000000, 5.0000000, 15.0000000, 6.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 4.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 11.0000000, 0.0000000, 0.0000000, 6.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 10.0000000, 5.0000000, 15.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 12.0000000, 14.0000000, 8.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 5.0000000, 11.0000000, 4.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 15.0000000, 16.0000000, 16.0000000, 11.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 9.0000000, 2.0000000, 12.0000000, 4.0000000, 0.0000000, 0.0000000, 6.0000000, 13.0000000, 0.0000000, 0.0000000, 6.0000000, 6.0000000, 0.0000000, 0.0000000, 3.0000000, 13.0000000, 0.0000000, 0.0000000, 5.0000000, 9.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 0.0000000, 0.0000000, 6.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 12.0000000, 8.0000000, 16.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 13.0000000, 12.0000000, 10.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 9.0000000, 15.0000000, 5.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 15.0000000, 15.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 14.0000000, 0.0000000, 9.0000000, 14.0000000, 0.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 12.0000000, 5.0000000, 0.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 10.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 14.0000000, 0.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 16.0000000, 7.0000000, 12.0000000, 16.0000000, 4.0000000, 0.0000000, 0.0000000, 0.0000000, 9.0000000, 16.0000000, 15.0000000, 7.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 4.0000000, 13.0000000, 12.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 15.0000000, 14.0000000, 16.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 4.0000000, 6.0000000, 16.0000000, 5.0000000, 0.0000000, 0.0000000, 8.0000000, 15.0000000, 1.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 12.0000000, 0.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 13.0000000, 0.0000000, 1.0000000, 13.0000000, 8.0000000, 0.0000000, 0.0000000, 1.0000000, 15.0000000, 10.0000000, 12.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 13.0000000, 4.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 3.0000000, 12.0000000, 11.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 14.0000000, 14.0000000, 15.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 2.0000000, 5.0000000, 16.0000000, 1.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 0.0000000, 0.0000000, 14.0000000, 6.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 0.0000000, 0.0000000, 11.0000000, 8.0000000, 0.0000000, 0.0000000, 3.0000000, 16.0000000, 2.0000000, 0.0000000, 10.0000000, 8.0000000, 0.0000000, 0.0000000, 0.0000000, 10.0000000, 15.0000000, 13.0000000, 16.0000000, 3.0000000, 0.0000000, 0.0000000, 0.0000000, 1.0000000, 15.0000000, 14.0000000, 6.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 4.0000000, 14.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 13.0000000, 16.0000000, 16.0000000, 10.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 4.0000000, 5.0000000, 16.0000000, 6.0000000, 0.0000000, 0.0000000, 8.0000000, 14.0000000, 0.0000000, 1.0000000, 15.0000000, 5.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 0.0000000, 0.0000000, 13.0000000, 4.0000000, 0.0000000, 0.0000000, 4.0000000, 15.0000000, 1.0000000, 7.0000000, 16.0000000, 1.0000000, 0.0000000, 0.0000000, 2.0000000, 15.0000000, 14.0000000, 16.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 13.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 10.0000000, 15.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 16.0000000, 14.0000000, 16.0000000, 7.0000000, 0.0000000, 0.0000000, 0.0000000, 4.0000000, 16.0000000, 8.0000000, 5.0000000, 16.0000000, 1.0000000, 0.0000000, 0.0000000, 5.0000000, 12.0000000, 0.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 8.0000000, 9.0000000, 0.0000000, 0.0000000, 12.0000000, 8.0000000, 0.0000000, 0.0000000, 5.0000000, 12.0000000, 0.0000000, 1.0000000, 15.0000000, 3.0000000, 0.0000000, 0.0000000, 4.0000000, 13.0000000, 4.0000000, 12.0000000, 13.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 9.0000000, 16.0000000, 13.0000000, 4.0000000, 0.0000000, 0.0000000}
+        , {0.0000000, 0.0000000, 2.0000000, 15.0000000, 10.0000000, 1.0000000, 0.0000000, 0.0000000, 0.0000000, 2.0000000, 13.0000000, 12.0000000, 14.0000000, 9.0000000, 0.0000000, 0.0000000, 0.0000000, 6.0000000, 16.0000000, 1.0000000, 1.0000000, 14.0000000, 2.0000000, 0.0000000, 0.0000000, 8.0000000, 16.0000000, 0.0000000, 0.0000000, 10.0000000, 5.0000000, 0.0000000, 0.0000000, 8.0000000, 14.0000000, 2.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 6.0000000, 14.0000000, 0.0000000, 0.0000000, 8.0000000, 8.0000000, 0.0000000, 0.0000000, 1.0000000, 14.0000000, 12.0000000, 8.0000000, 15.0000000, 6.0000000, 0.0000000, 0.0000000, 0.0000000, 3.0000000, 13.0000000, 16.0000000, 8.0000000, 1.0000000, 0.0000000}
+};
diff --git a/E2T/iris.h b/E2T/iris.h
new file mode 100644
index 0000000..d4c85f5
--- /dev/null
+++ b/E2T/iris.h
@@ -0,0 +1,109 @@
+#define POSITIVE_SAMPLES 50
+#define NEGATIVE_SAMPLES 50
+#define Feature_dimension 4
+
+float X_positive[POSITIVE_SAMPLES][Feature_dimension] = {
+        {5.1,3.5,1.4,.2},
+        {4.9,3,1.4,.2},
+        {4.7,3.2,1.3,.2},
+        {4.6,3.1,1.5,.2},
+        {5,3.6,1.4,.2},
+        {5.4,3.9,1.7,.4},
+        {4.6,3.4,1.4,.3},
+        {5,3.4,1.5,.2},
+        {4.4,2.9,1.4,.2},
+        {4.9,3.1,1.5,.1},
+        {5.4,3.7,1.5,.2},
+        {4.8,3.4,1.6,.2},
+        {4.8,3,1.4,.1},
+        {4.3,3,1.1,.1},
+        {5.8,4,1.2,.2},
+        {5.7,4.4,1.5,.4},
+        {5.4,3.9,1.3,.4},
+        {5.1,3.5,1.4,.3},
+        {5.7,3.8,1.7,.3},
+        {5.1,3.8,1.5,.3},
+        {5.4,3.4,1.7,.2},
+        {5.1,3.7,1.5,.4},
+        {4.6,3.6,1,.2},
+        {5.1,3.3,1.7,.5},
+        {4.8,3.4,1.9,.2},
+        {5,3,1.6,.2},
+        {5,3.4,1.6,.4},
+        {5.2,3.5,1.5,.2},
+        {5.2,3.4,1.4,.2},
+        {4.7,3.2,1.6,.2},
+        {4.8,3.1,1.6,.2},
+        {5.4,3.4,1.5,.4},
+        {5.2,4.1,1.5,.1},
+        {5.5,4.2,1.4,.2},
+        {4.9,3.1,1.5,.2},
+        {5,3.2,1.2,.2},
+        {5.5,3.5,1.3,.2},
+        {4.9,3.6,1.4,.1},
+        {4.4,3,1.3,.2},
+        {5.1,3.4,1.5,.2},
+        {5,3.5,1.3,.3},
+        {4.5,2.3,1.3,.3},
+        {4.4,3.2,1.3,.2},
+        {5,3.5,1.6,.6},
+        {5.1,3.8,1.9,.4},
+        {4.8,3,1.4,.3},
+        {5.1,3.8,1.6,.2},
+        {4.6,3.2,1.4,.2},
+        {5.3,3.7,1.5,.2},
+        {5,3.3,1.4,.2}
+};
+
+float X_negative[NEGATIVE_SAMPLES][Feature_dimension] = {
+        {7,3.2,4.7,1.4},
+        {6.4,3.2,4.5,1.5},
+        {6.9,3.1,4.9,1.5},
+        {5.5,2.3,4,1.3},
+        {6.5,2.8,4.6,1.5},
+        {5.7,2.8,4.5,1.3},
+        {6.3,3.3,4.7,1.6},
+        {4.9,2.4,3.3,1},
+        {6.6,2.9,4.6,1.3},
+        {5.2,2.7,3.9,1.4},
+        {5,2,3.5,1},
+        {5.9,3,4.2,1.5},
+        {6,2.2,4,1},
+        {6.1,2.9,4.7,1.4},
+        {5.6,2.9,3.6,1.3},
+        {6.7,3.1,4.4,1.4},
+        {5.6,3,4.5,1.5},
+        {5.8,2.7,4.1,1},
+        {6.2,2.2,4.5,1.5},
+        {5.6,2.5,3.9,1.1},
+        {5.9,3.2,4.8,1.8},
+        {6.1,2.8,4,1.3},
+        {6.3,2.5,4.9,1.5},
+        {6.1,2.8,4.7,1.2},
+        {6.4,2.9,4.3,1.3},
+        {6.6,3,4.4,1.4},
+        {6.8,2.8,4.8,1.4},
+        {6.7,3,5,1.7},
+        {6,2.9,4.5,1.5},
+        {5.7,2.6,3.5,1},
+        {5.5,2.4,3.8,1.1},
+        {5.5,2.4,3.7,1},
+        {5.8,2.7,3.9,1.2},
+        {6,2.7,5.1,1.6},
+        {5.4,3,4.5,1.5},
+        {6,3.4,4.5,1.6},
+        {6.7,3.1,4.7,1.5},
+        {6.3,2.3,4.4,1.3},
+        {5.6,3,4.1,1.3},
+        {5.5,2.5,4,1.3},
+        {5.5,2.6,4.4,1.2},
+        {6.1,3,4.6,1.4},
+        {5.8,2.6,4,1.2},
+        {5,2.3,3.3,1},
+        {5.6,2.7,4.2,1.3},
+        {5.7,3,4.2,1.2},
+        {5.7,2.9,4.2,1.3},
+        {6.2,2.9,4.3,1.3},
+        {5.1,2.5,3,1.1},
+        {5.7,2.8,4.1,1.3}
+};