THE QUESTION: Could someone please modify this code so that it analyzes stock ma
ID: 3838198 • Letter: T
Question
THE QUESTION: Could someone please modify this code so that it analyzes stock market data instead of sunspots? (The code should be written in C. Or at least tell/comment to code so that we know the areas to change and how to change them.) 2. Find a data set with sufficient data to train and test your neural network and decide how you will form the training set and testing set. (5%) 3. Modify the given backpropagation neural network to read in the training data. (15%) 4. Train your neural network using the training data from step 2. (10%) 5. Test your trained neural network using the test data from step 2 and output the performance of your classifier as a confusion matrix. (10%) 6. Repeat steps 4 and 5 by modifying the parameters of your network appropriately to achieve a probability of correct classification of 0.9 or higher (i.e., the main diagonal of your confusion matrix should be 0.9 or higher). (50%) HOW DO I GO ABOUT DOING THIS? If more time is needed that is okay too. /****************************************************************************** ==================================================== Network: Backpropagation Network with Bias Terms and Momentum ==================================================== Application: Time-Series Forecasting Prediction of the Annual Number of Sunspots Author: Karsten Kutza Date: 17.4.96 Reference: D.E. Rumelhart, G.E. Hinton, R.J. Williams Learning Internal Representations by Error Propagation in: D.E. Rumelhart, J.L. McClelland (Eds.) Parallel Distributed Processing, Volume 1 MIT Press, Cambridge, MA, pp. 318-362, 1986 ******************************************************************************/ /****************************************************************************** D E C L A R A T I O N S ******************************************************************************/ #include #include #include typedef int BOOL; typedef int INT; typedef double REAL; #define FALSE 0 #define TRUE 1 #define NOT ! #define AND && #define OR || #define MIN_REAL -HUGE_VAL #define MAX_REAL +HUGE_VAL #define MIN(x,y) ((x)<(y) ? (x) : (y)) #define MAX(x,y) ((x)>(y) ? (x) : (y)) #define LO 0.1 #define HI 0.9 #define BIAS 1 #define sqr(x) ((x)*(x)) typedef struct { /* A LAYER OF A NET: */ INT Units; /* - number of units in this layer */ REAL* Output; /* - output of ith unit */ REAL* Error; /* - error term of ith unit */ REAL** Weight; /* - connection weights to ith unit */ REAL** WeightSave; /* - saved weights for stopped training */ REAL** dWeight; /* - last weight deltas for momentum */ } LAYER; typedef struct { /* A NET: */ LAYER** Layer; /* - layers of this net */ LAYER* InputLayer; /* - input layer */ LAYER* OutputLayer; /* - output layer */ REAL Alpha; /* - momentum factor */ REAL Eta; /* - learning rate */ REAL Gain; /* - gain of sigmoid function */ REAL Error; /* - total net error */ } NET; /****************************************************************************** R A N D O M S D R A W N F R O M D I S T R I B U T I O N S ******************************************************************************/ void InitializeRandoms() { srand(4711); } INT RandomEqualINT(INT Low, INT High) { return rand() % (High-Low+1) + Low; } REAL RandomEqualREAL(REAL Low, REAL High) { return ((REAL) rand() / RAND_MAX) * (High-Low) + Low; } /****************************************************************************** A P P L I C A T I O N - S P E C I F I C C O D E ******************************************************************************/ #define NUM_LAYERS 3 #define N 30 #define M 1 INT Units[NUM_LAYERS] = {N, 10, M}; #define FIRST_YEAR 1700 #define NUM_YEARS 280 #define TRAIN_LWB (N) #define TRAIN_UPB (179) #define TRAIN_YEARS (TRAIN_UPB - TRAIN_LWB + 1) #define TEST_LWB (180) #define TEST_UPB (259) #define TEST_YEARS (TEST_UPB - TEST_LWB + 1) #define EVAL_LWB (260) #define EVAL_UPB (NUM_YEARS - 1) #define EVAL_YEARS (EVAL_UPB - EVAL_LWB + 1) REAL Sunspots_[NUM_YEARS]; REAL Sunspots [NUM_YEARS] = { 0.0262, 0.0575, 0.0837, 0.1203, 0.1883, 0.3033, 0.1517, 0.1046, 0.0523, 0.0418, 0.0157, 0.0000, 0.0000, 0.0105, 0.0575, 0.1412, 0.2458, 0.3295, 0.3138, 0.2040, 0.1464, 0.1360, 0.1151, 0.0575, 0.1098, 0.2092, 0.4079, 0.6381, 0.5387, 0.3818, 0.2458, 0.1831, 0.0575, 0.0262, 0.0837, 0.1778, 0.3661, 0.4236, 0.5805, 0.5282, 0.3818, 0.2092, 0.1046, 0.0837, 0.0262, 0.0575, 0.1151, 0.2092, 0.3138, 0.4231, 0.4362, 0.2495, 0.2500, 0.1606, 0.0638, 0.0502, 0.0534, 0.1700, 0.2489, 0.2824, 0.3290, 0.4493, 0.3201, 0.2359, 0.1904, 0.1093, 0.0596, 0.1977, 0.3651, 0.5549, 0.5272, 0.4268, 0.3478, 0.1820, 0.1600, 0.0366, 0.1036, 0.4838, 0.8075, 0.6585, 0.4435, 0.3562, 0.2014, 0.1192, 0.0534, 0.1260, 0.4336, 0.6904, 0.6846, 0.6177, 0.4702, 0.3483, 0.3138, 0.2453, 0.2144, 0.1114, 0.0837, 0.0335, 0.0214, 0.0356, 0.0758, 0.1778, 0.2354, 0.2254, 0.2484, 0.2207, 0.1470, 0.0528, 0.0424, 0.0131, 0.0000, 0.0073, 0.0262, 0.0638, 0.0727, 0.1851, 0.2395, 0.2150, 0.1574, 0.1250, 0.0816, 0.0345, 0.0209, 0.0094, 0.0445, 0.0868, 0.1898, 0.2594, 0.3358, 0.3504, 0.3708, 0.2500, 0.1438, 0.0445, 0.0690, 0.2976, 0.6354, 0.7233, 0.5397, 0.4482, 0.3379, 0.1919, 0.1266, 0.0560, 0.0785, 0.2097, 0.3216, 0.5152, 0.6522, 0.5036, 0.3483, 0.3373, 0.2829, 0.2040, 0.1077, 0.0350, 0.0225, 0.1187, 0.2866, 0.4906, 0.5010, 0.4038, 0.3091, 0.2301, 0.2458, 0.1595, 0.0853, 0.0382, 0.1966, 0.3870, 0.7270, 0.5816, 0.5314, 0.3462, 0.2338, 0.0889, 0.0591, 0.0649, 0.0178, 0.0314, 0.1689, 0.2840, 0.3122, 0.3332, 0.3321, 0.2730, 0.1328, 0.0685, 0.0356, 0.0330, 0.0371, 0.1862, 0.3818, 0.4451, 0.4079, 0.3347, 0.2186, 0.1370, 0.1396, 0.0633, 0.0497, 0.0141, 0.0262, 0.1276, 0.2197, 0.3321, 0.2814, 0.3243, 0.2537, 0.2296, 0.0973, 0.0298, 0.0188, 0.0073, 0.0502, 0.2479, 0.2986, 0.5434, 0.4215, 0.3326, 0.1966, 0.1365, 0.0743, 0.0303, 0.0873, 0.2317, 0.3342, 0.3609, 0.4069, 0.3394, 0.1867, 0.1109, 0.0581, 0.0298, 0.0455, 0.1888, 0.4168, 0.5983, 0.5732, 0.4644, 0.3546, 0.2484, 0.1600, 0.0853, 0.0502, 0.1736, 0.4843, 0.7929, 0.7128, 0.7045, 0.4388, 0.3630, 0.1647, 0.0727, 0.0230, 0.1987, 0.7411, 0.9947, 0.9665, 0.8316, 0.5873, 0.2819, 0.1961, 0.1459, 0.0534, 0.0790, 0.2458, 0.4906, 0.5539, 0.5518, 0.5465, 0.3483, 0.3603, 0.1987, 0.1804, 0.0811, 0.0659, 0.1428, 0.4838, 0.8127 }; REAL Mean; REAL TrainError; REAL TrainErrorPredictingMean; REAL TestError; REAL TestErrorPredictingMean; FILE* f; void NormalizeSunspots() { INT Year; REAL Min, Max; Min = MAX_REAL; Max = MIN_REAL; for (Year=0; YearAlpha = 0.5; Net->Eta = 0.05; Net->Gain = 1; NormalizeSunspots(); TrainErrorPredictingMean = 0; for (Year=TRAIN_LWB; Year<=TRAIN_UPB; Year++) { for (i=0; iLayer = (LAYER**) calloc(NUM_LAYERS, sizeof(LAYER*)); for (l=0; lLayer[l] = (LAYER*) malloc(sizeof(LAYER)); Net->Layer[l]->Units = Units[l]; Net->Layer[l]->Output = (REAL*) calloc(Units[l]+1, sizeof(REAL)); Net->Layer[l]->Error = (REAL*) calloc(Units[l]+1, sizeof(REAL)); Net->Layer[l]->Weight = (REAL**) calloc(Units[l]+1, sizeof(REAL*)); Net->Layer[l]->WeightSave = (REAL**) calloc(Units[l]+1, sizeof(REAL*)); Net->Layer[l]->dWeight = (REAL**) calloc(Units[l]+1, sizeof(REAL*)); Net->Layer[l]->Output[0] = BIAS; if (l != 0) { for (i=1; i<=Units[l]; i++) { Net->Layer[l]->Weight[i] = (REAL*) calloc(Units[l-1]+1, sizeof(REAL)); Net->Layer[l]->WeightSave[i] = (REAL*) calloc(Units[l-1]+1, sizeof(REAL)); Net->Layer[l]->dWeight[i] = (REAL*) calloc(Units[l-1]+1, sizeof(REAL)); } } } Net->InputLayer = Net->Layer[0]; Net->OutputLayer = Net->Layer[NUM_LAYERS - 1]; Net->Alpha = 0.9; Net->Eta = 0.25; Net->Gain = 1; } void RandomWeights(NET* Net) { INT l,i,j; for (l=1; lLayer[l]->Units; i++) { for (j=0; j<=Net->Layer[l-1]->Units; j++) { Net->Layer[l]->Weight[i][j] = RandomEqualREAL(-0.5, 0.5); } } } } void SetInput(NET* Net, REAL* Input) { INT i; for (i=1; i<=Net->InputLayer->Units; i++) { Net->InputLayer->Output[i] = Input[i-1]; } } void GetOutput(NET* Net, REAL* Output) { INT i; for (i=1; i<=Net->OutputLayer->Units; i++) { Output[i-1] = Net->OutputLayer->Output[i]; } } /****************************************************************************** S U P P O R T F O R S T O P P E D T R A I N I N G ******************************************************************************/ void SaveWeights(NET* Net) { INT l,i,j; for (l=1; lLayer[l]->Units; i++) { for (j=0; j<=Net->Layer[l-1]->Units; j++) { Net->Layer[l]->WeightSave[i][j] = Net->Layer[l]->Weight[i][j]; } } } } void RestoreWeights(NET* Net) { INT l,i,j; for (l=1; lLayer[l]->Units; i++) { for (j=0; j<=Net->Layer[l-1]->Units; j++) { Net->Layer[l]->Weight[i][j] = Net->Layer[l]->WeightSave[i][j]; } } } } /****************************************************************************** P R O P A G A T I N G S I G N A L S ******************************************************************************/ void PropagateLayer(NET* Net, LAYER* Lower, LAYER* Upper) { INT i,j; REAL Sum; for (i=1; i<=Upper->Units; i++) { Sum = 0; for (j=0; j<=Lower->Units; j++) { Sum += Upper->Weight[i][j] * Lower->Output[j]; } Upper->Output[i] = 1 / (1 + exp(-Net->Gain * Sum)); } } void PropagateNet(NET* Net) { INT l; for (l=0; lLayer[l], Net->Layer[l+1]); } } /****************************************************************************** B A C K P R O P A G A T I N G E R R O R S ******************************************************************************/ void ComputeOutputError(NET* Net, REAL* Target) { INT i; REAL Out, Err; Net->Error = 0; for (i=1; i<=Net->OutputLayer->Units; i++) { Out = Net->OutputLayer->Output[i]; Err = Target[i-1]-Out; Net->OutputLayer->Error[i] = Net->Gain * Out * (1-Out) * Err; Net->Error += 0.5 * sqr(Err); } } void BackpropagateLayer(NET* Net, LAYER* Upper, LAYER* Lower) { INT i,j; REAL Out, Err; for (i=1; i<=Lower->Units; i++) { Out = Lower->Output[i]; Err = 0; for (j=1; j<=Upper->Units; j++) { Err += Upper->Weight[j][i] * Upper->Error[j]; } Lower->Error[i] = Net->Gain * Out * (1-Out) * Err; } } void BackpropagateNet(NET* Net) { INT l; for (l=NUM_LAYERS-1; l>1; l--) { BackpropagateLayer(Net, Net->Layer[l], Net->Layer[l-1]); } } void AdjustWeights(NET* Net) { INT l,i,j; REAL Out, Err, dWeight; for (l=1; lLayer[l]->Units; i++) { for (j=0; j<=Net->Layer[l-1]->Units; j++) { Out = Net->Layer[l-1]->Output[j]; Err = Net->Layer[l]->Error[i]; dWeight = Net->Layer[l]->dWeight[i][j]; Net->Layer[l]->Weight[i][j] += Net->Eta * Err * Out + Net->Alpha * dWeight; Net->Layer[l]->dWeight[i][j] = Net->Eta * Err * Out; } } } } /****************************************************************************** S I M U L A T I N G T H E N E T ******************************************************************************/ void SimulateNet(NET* Net, REAL* Input, REAL* Output, REAL* Target, BOOL Training) { SetInput(Net, Input); PropagateNet(Net); GetOutput(Net, Output); ComputeOutputError(Net, Target); if (Training) { BackpropagateNet(Net); AdjustWeights(Net); } } void TrainNet(NET* Net, INT Epochs) { INT Year, n; REAL Output[M]; for (n=0; nError; } TestError = 0; for (Year=TEST_LWB; Year<=TEST_UPB; Year++) { SimulateNet(Net, &(Sunspots[Year-N]), Output, &(Sunspots[Year]), FALSE); TestError += Net->Error; } fprintf(f, " NMSE is %0.3f on Training Set and %0.3f on Test Set", TrainError / TrainErrorPredictingMean, TestError / TestErrorPredictingMean); } void EvaluateNet(NET* Net) { INT Year; REAL Output [M]; REAL Output_[M]; fprintf(f, " "); fprintf(f, "Year Sunspots Open-Loop Prediction Closed-Loop Prediction "); fprintf(f, " "); for (Year=EVAL_LWB; Year<=EVAL_UPB; Year++) { SimulateNet(Net, &(Sunspots [Year-N]), Output, &(Sunspots [Year]), FALSE); SimulateNet(Net, &(Sunspots_[Year-N]), Output_, &(Sunspots_[Year]), FALSE); Sunspots_[Year] = Output_[0]; fprintf(f, "%d %0.3f %0.3f %0.3f ", FIRST_YEAR + Year, Sunspots[Year], Output [0], Output_[0]); } } /****************************************************************************** M A I N ******************************************************************************/ int main() { NET Net; BOOL Stop; REAL MinTestError; InitializeRandoms(); GenerateNetwork(&Net); RandomWeights(&Net); InitializeApplication(&Net); Stop = FALSE; MinTestError = MAX_REAL; do { TrainNet(&Net, 10); TestNet(&Net); if (TestError < MinTestError) { fprintf(f, " - saving Weights ..."); MinTestError = TestError; SaveWeights(&Net); } else if (TestError > 1.2 * MinTestError) { fprintf(f, " - stopping Training and restoring Weights ..."); Stop = TRUE; RestoreWeights(&Net); } } while (NOT Stop); TestNet(&Net); EvaluateNet(&Net); FinalizeApplication(&Net); }
Explanation / Answer
#include #include #include typedef int BOOL; typedef int INT; typedef double REAL; #define FALSE 0 #define TRUE 1 #define NOT ! #define AND && #define OR || #define MIN_REAL -HUGE_VAL #define MAX_REAL +HUGE_VAL #define MIN(x,y) ((x)(y) ? (x) : (y)) #define LO 0.1 #define HI 0.9 #define BIAS 1 #define sqr(x) ((x)*(x)) typedef struct { /* A LAYER OF A NET: */ INT Units; /* - number of units in this layer */ REAL* Output; /* - output of ith unit */ REAL* Error; /* - error term of ith unit */ REAL** Weight; /* - connection weights to ith unit */ REAL** WeightSave; /* - saved weights for stopped training */ REAL** dWeight; /* - last weight deltas for momentum */ } LAYER; typedef struct { /* A NET: */ LAYER** Layer; /* - layers of this net */ LAYER* InputLayer; /* - input layer */ LAYER* OutputLayer; /* - output layer */ REAL Alpha; /* - momentum factor */ REAL Eta; /* - learning rate */ REAL Gain; /* - gain of sigmoid function */ REAL Error; /* - total net error */ } NET; /****************************************************************************** R A N D O M S D R A W N F R O M D I S T R I B U T I O N S ******************************************************************************/ void InitializeRandoms() { srand(4711); } INT RandomEqualINT(INT Low, INT High) { return rand() % (High-Low+1) + Low; } REAL RandomEqualREAL(REAL Low, REAL High) { return ((REAL) rand() / RAND_MAX) * (High-Low) + Low; } /****************************************************************************** A P P L I C A T I O N - S P E C I F I C C O D E ******************************************************************************/ #define NUM_LAYERS 3 #define N 30 #define M 1 INT Units[NUM_LAYERS] = {N, 10, M}; #define FIRST_YEAR 1700 #define NUM_YEARS 280 #define TRAIN_LWB (N) #define TRAIN_UPB (179) #define TRAIN_YEARS (TRAIN_UPB - TRAIN_LWB + 1) #define TEST_LWB (180) #define TEST_UPB (259) #define TEST_YEARS (TEST_UPB - TEST_LWB + 1) #define EVAL_LWB (260) #define EVAL_UPB (NUM_YEARS - 1) #define EVAL_YEARS (EVAL_UPB - EVAL_LWB + 1) REAL Sunspots_[NUM_YEARS]; REAL Sunspots [NUM_YEARS] = { 0.0262, 0.0575, 0.0837, 0.1203, 0.1883, 0.3033, 0.1517, 0.1046, 0.0523, 0.0418, 0.0157, 0.0000, 0.0000, 0.0105, 0.0575, 0.1412, 0.2458, 0.3295, 0.3138, 0.2040, 0.1464, 0.1360, 0.1151, 0.0575, 0.1098, 0.2092, 0.4079, 0.6381, 0.5387, 0.3818, 0.2458, 0.1831, 0.0575, 0.0262, 0.0837, 0.1778, 0.3661, 0.4236, 0.5805, 0.5282, 0.3818, 0.2092, 0.1046, 0.0837, 0.0262, 0.0575, 0.1151, 0.2092, 0.3138, 0.4231, 0.4362, 0.2495, 0.2500, 0.1606, 0.0638, 0.0502, 0.0534, 0.1700, 0.2489, 0.2824, 0.3290, 0.4493, 0.3201, 0.2359, 0.1904, 0.1093, 0.0596, 0.1977, 0.3651, 0.5549, 0.5272, 0.4268, 0.3478, 0.1820, 0.1600, 0.0366, 0.1036, 0.4838, 0.8075, 0.6585, 0.4435, 0.3562, 0.2014, 0.1192, 0.0534, 0.1260, 0.4336, 0.6904, 0.6846, 0.6177, 0.4702, 0.3483, 0.3138, 0.2453, 0.2144, 0.1114, 0.0837, 0.0335, 0.0214, 0.0356, 0.0758, 0.1778, 0.2354, 0.2254, 0.2484, 0.2207, 0.1470, 0.0528, 0.0424, 0.0131, 0.0000, 0.0073, 0.0262, 0.0638, 0.0727, 0.1851, 0.2395, 0.2150, 0.1574, 0.1250, 0.0816, 0.0345, 0.0209, 0.0094, 0.0445, 0.0868, 0.1898, 0.2594, 0.3358, 0.3504, 0.3708, 0.2500, 0.1438, 0.0445, 0.0690, 0.2976, 0.6354, 0.7233, 0.5397, 0.4482, 0.3379, 0.1919, 0.1266, 0.0560, 0.0785, 0.2097, 0.3216, 0.5152, 0.6522, 0.5036, 0.3483, 0.3373, 0.2829, 0.2040, 0.1077, 0.0350, 0.0225, 0.1187, 0.2866, 0.4906, 0.5010, 0.4038, 0.3091, 0.2301, 0.2458, 0.1595, 0.0853, 0.0382, 0.1966, 0.3870, 0.7270, 0.5816, 0.5314, 0.3462, 0.2338, 0.0889, 0.0591, 0.0649, 0.0178, 0.0314, 0.1689, 0.2840, 0.3122, 0.3332, 0.3321, 0.2730, 0.1328, 0.0685, 0.0356, 0.0330, 0.0371, 0.1862, 0.3818, 0.4451, 0.4079, 0.3347, 0.2186, 0.1370, 0.1396, 0.0633, 0.0497, 0.0141, 0.0262, 0.1276, 0.2197, 0.3321, 0.2814, 0.3243, 0.2537, 0.2296, 0.0973, 0.0298, 0.0188, 0.0073, 0.0502, 0.2479, 0.2986, 0.5434, 0.4215, 0.3326, 0.1966, 0.1365, 0.0743, 0.0303, 0.0873, 0.2317, 0.3342, 0.3609, 0.4069, 0.3394, 0.1867, 0.1109, 0.0581, 0.0298, 0.0455, 0.1888, 0.4168, 0.5983, 0.5732, 0.4644, 0.3546, 0.2484, 0.1600, 0.0853, 0.0502, 0.1736, 0.4843, 0.7929, 0.7128, 0.7045, 0.4388, 0.3630, 0.1647, 0.0727, 0.0230, 0.1987, 0.7411, 0.9947, 0.9665, 0.8316, 0.5873, 0.2819, 0.1961, 0.1459, 0.0534, 0.0790, 0.2458, 0.4906, 0.5539, 0.5518, 0.5465, 0.3483, 0.3603, 0.1987, 0.1804, 0.0811, 0.0659, 0.1428, 0.4838, 0.8127 }; REAL Mean; REAL TrainError; REAL TrainErrorPredictingMean; REAL TestError; REAL TestErrorPredictingMean; FILE* f; void NormalizeSunspots() { INT Year; REAL Min, Max; Min = MAX_REAL; Max = MIN_REAL; for (Year=0; YearAlpha = 0.5; Net->Eta = 0.05; Net->Gain = 1; NormalizeSunspots(); TrainErrorPredictingMean = 0; for (Year=TRAIN_LWB; YearLayer[l]->Units = Units[l]; Net->Layer[l]->Output = (REAL*) calloc(Units[l]+1, sizeof(REAL)); Net->Layer[l]->Error = (REAL*) calloc(Units[l]+1, sizeof(REAL)); Net->Layer[l]->Weight = (REAL**) calloc(Units[l]+1, sizeof(REAL*)); Net->Layer[l]->WeightSave = (REAL**) calloc(Units[l]+1, sizeof(REAL*)); Net->Layer[l]->dWeight = (REAL**) calloc(Units[l]+1, sizeof(REAL*)); Net->Layer[l]->Output[0] = BIAS; if (l != 0) { for (i=1; iLayer[l]->Weight[i] = (REAL*) calloc(Units[l-1]+1, sizeof(REAL)); Net->Layer[l]->WeightSave[i] = (REAL*) calloc(Units[l-1]+1, sizeof(REAL)); Net->Layer[l]->dWeight[i] = (REAL*) calloc(Units[l-1]+1, sizeof(REAL)); } } } Net->InputLayer = Net->Layer[0]; Net->OutputLayer = Net->Layer[NUM_LAYERS - 1]; Net->Alpha = 0.9; Net->Eta = 0.25; Net->Gain = 1; } void RandomWeights(NET* Net) { INT l,i,j; for (l=1; lLayer[l]->Units; i++) { for (j=0; jLayer[l-1]->Units; j++) { Net->Layer[l]->Weight[i][j] = RandomEqualREAL(-0.5, 0.5); } } } } void SetInput(NET* Net, REAL* Input) { INT i; for (i=1; iInputLayer->Units; i++) { Net->InputLayer->Output[i] = Input[i-1]; } } void GetOutput(NET* Net, REAL* Output) { INT i; for (i=1; iOutputLayer->Units; i++) { Output[i-1] = Net->OutputLayer->Output[i]; } } /****************************************************************************** S U P P O R T F O R S T O P P E D T R A I N I N G ******************************************************************************/ void SaveWeights(NET* Net) { INT l,i,j; for (l=1; lLayer[l]->Units; i++) { for (j=0; jLayer[l-1]->Units; j++) { Net->Layer[l]->WeightSave[i][j] = Net->Layer[l]->Weight[i][j]; } } } } void RestoreWeights(NET* Net) { INT l,i,j; for (l=1; lLayer[l]->Units; i++) { for (j=0; jLayer[l-1]->Units; j++) { Net->Layer[l]->Weight[i][j] = Net->Layer[l]->WeightSave[i][j]; } } } } /****************************************************************************** P R O P A G A T I N G S I G N A L S ******************************************************************************/ void PropagateLayer(NET* Net, LAYER* Lower, LAYER* Upper) { INT i,j; REAL Sum; for (i=1; iUnits; i++) { Sum = 0; for (j=0; jUnits; j++) { Sum += Upper->Weight[i][j] * Lower->Output[j]; } Upper->Output[i] = 1 / (1 + exp(-Net->Gain * Sum)); } } void PropagateNet(NET* Net) { INT l; for (l=0; lLayer[l], Net->Layer[l+1]); } } /****************************************************************************** B A C K P R O P A G A T I N G E R R O R S ******************************************************************************/ void ComputeOutputError(NET* Net, REAL* Target) { INT i; REAL Out, Err; Net->Error = 0; for (i=1; iOutputLayer->Units; i++) { Out = Net->OutputLayer->Output[i]; Err = Target[i-1]-Out; Net->OutputLayer->Error[i] = Net->Gain * Out * (1-Out) * Err; Net->Error += 0.5 * sqr(Err); } } void BackpropagateLayer(NET* Net, LAYER* Upper, LAYER* Lower) { INT i,j; REAL Out, Err; for (i=1; iUnits; i++) { Out = Lower->Output[i]; Err = 0; for (j=1; jUnits; j++) { Err += Upper->Weight[j][i] * Upper->Error[j]; } Lower->Error[i] = Net->Gain * Out * (1-Out) * Err; } } void BackpropagateNet(NET* Net) { INT l; for (l=NUM_LAYERS-1; l>1; l--) { BackpropagateLayer(Net, Net->Layer[l], Net->Layer[l-1]); } } void AdjustWeights(NET* Net) { INT l,i,j; REAL Out, Err, dWeight; for (l=1; lLayer[l]->Units; i++) { for (j=0; jLayer[l-1]->Units; j++) { Out = Net->Layer[l-1]->Output[j]; Err = Net->Layer[l]->Error[i]; dWeight = Net->Layer[l]->dWeight[i][j]; Net->Layer[l]->Weight[i][j] += Net->Eta * Err * Out + Net->Alpha * dWeight; Net->Layer[l]->dWeight[i][j] = Net->Eta * Err * Out; } } } } /****************************************************************************** S I M U L A T I N G T H E N E T ******************************************************************************/ void SimulateNet(NET* Net, REAL* Input, REAL* Output, REAL* Target, BOOL Training) { SetInput(Net, Input); PropagateNet(Net); GetOutput(Net, Output); ComputeOutputError(Net, Target); if (Training) { BackpropagateNet(Net); AdjustWeights(Net); } } void TrainNet(NET* Net, INT Epochs) { INT Year, n; REAL Output[M]; for (n=0; nError; } TestError = 0; for (Year=TEST_LWB; YearError; } fprintf(f, " NMSE is %0.3f on Training Set and %0.3f on Test Set", TrainError / TrainErrorPredictingMean, TestError / TestErrorPredictingMean); } void EvaluateNet(NET* Net) { INT Year; REAL Output [M]; REAL Output_[M]; fprintf(f, " "); fprintf(f, "Year Sunspots Open-Loop Prediction Closed-Loop Prediction "); fprintf(f, " "); for (Year=EVAL_LWB; YearRelated Questions
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