Weather casting with Machine Learning (SVM and SRNN).
Dependencies: EthernetInterface GraphicHandler NTPClient SRNN SVM SensorModule mbed-rtos mbed
setup.cpp
- Committer:
- yukari_hinata
- Date:
- 2015-02-19
- Revision:
- 3:5add3759e08a
- Parent:
- 2:20ecfe6edd71
- Child:
- 4:00da8e8c7e2a
File content as of revision 3:5add3759e08a:
#include "setup.hpp" // mcsvmのセットアップ : サンプル/係数のセット static void mcsvm_setup(void) { FILE* svm_setup_fp; char buf_str[20]; int ret, line; float buf_data[DIM_SIGNAL]; float* svm_tmp_sample = new float[MCSVM_NUM_SAMPLES * DIM_SIGNAL]; int* svm_tmp_sample_label = new int[MCSVM_NUM_SAMPLES]; float* svm_tmp_mc_alpha = new float[MCSVM_NUM_SAMPLES * NUM_WEATHERS * (NUM_WEATHERS - 1) / 2]; svm_setup_fp = fopen( "/local/SVM_SAMP.CSV" , "r" ); if( svm_setup_fp == NULL ) { fprintf( stderr, "Error in svm setup : sample file cannot open. \r \n" ); exit(1); } line = 0; while( ( ret = fscanf( svm_setup_fp, " %[^\n,],%f,%f,%f", buf_str, &(buf_data[0]), &(buf_data[1]), &(buf_data[2])) ) != EOF ) { if ( !strcmp(buf_str,"shiny") ) { svm_tmp_sample_label[line] = SHINY; } else if ( !strcmp(buf_str,"cloudy") ) { svm_tmp_sample_label[line] = CLOUDY; } else if ( !strcmp(buf_str,"rainy") ) { svm_tmp_sample_label[line] = RAINY; } else if ( !strcmp(buf_str,"snowy") ) { svm_tmp_sample_label[line] = SNOWY; } else { continue; } memcpy(&(svm_tmp_sample[line * DIM_SIGNAL]), buf_data, sizeof(float) * DIM_SIGNAL); // printf("svm sample loading.... ret : %d line : %d %s %f %f %f \r\n", ret, line, buf_str, svm_tmp_sample[line*3], svm_tmp_sample[line*3+1], svm_tmp_sample[line*3+2]); line++; } mcsvm = new MCSVM(NUM_WEATHERS, DIM_SIGNAL, MCSVM_NUM_SAMPLES, svm_tmp_sample, svm_tmp_sample_label); // Thank you freopen. // Here, we should not use fclose -> fopen svm_setup_fp = freopen("/local/SVM_ALPH.CSV", "r", svm_setup_fp ); fflush( svm_setup_fp ); // required. if ( svm_setup_fp == NULL ) { fprintf( stderr, "Error in open learned alpha data. \r\n"); exit(1); } // 一列のデータではfscanfフォーマットがだるいので, fgetsを使用 line = 0; while( fgets( buf_str, 20, svm_setup_fp ) != NULL ) { svm_tmp_mc_alpha[line] = atof(buf_str); // printf("%d %f \r\n", line, tmp_mc_alpha[line]); line++; } fclose( svm_setup_fp ); mcsvm->set_alpha(svm_tmp_mc_alpha, MCSVM_NUM_SAMPLES, NUM_WEATHERS); delete [] svm_tmp_sample; delete [] svm_tmp_sample_label; delete [] svm_tmp_mc_alpha; } // SRNNのセットアップ. 初期データのセット. static void srnn_setup(void) { FILE* srnn_setup_fp; int ret; float buf_data[DIM_SIGNAL]; float sample[LEN_DATA_SEQUENCE * DIM_SIGNAL]; float sample_maxmin[DIM_SIGNAL * 2]; char buf_str[20]; // 信号の正規化のために, 信号の最大値と最小値を決めてやる必要がある. sample_maxmin[0] = 50; sample_maxmin[1] = -20; // 気温の最大/最小値(想定値) sample_maxmin[2] = 1030; sample_maxmin[3] = 960; // 気圧 sample_maxmin[4] = 100; sample_maxmin[5] = 0; // 湿度 srnn_setup_fp = fopen( SEQUENCE_DATA_NAME, "r"); if( srnn_setup_fp == NULL ) { fprintf( stderr, "Error in SRNN setup. sample file cannot open. \r\n"); exit(1); } int line = 0; while( ( ret = fscanf( srnn_setup_fp, " %[^\n,],%f,%f,%f", buf_str, &(buf_data[0]), &(buf_data[1]), &(buf_data[2])) ) != EOF ) { memcpy(&(sample[line * DIM_SIGNAL]), buf_data, sizeof(float) * DIM_SIGNAL); // printf("sample %d : %f %f %f \r\n", line, MATRIX_AT(sample,DIM_SIGNAL,line,0), MATRIX_AT(sample,DIM_SIGNAL,line,1), MATRIX_AT(sample,DIM_SIGNAL,line,2)); line++; } fclose( srnn_setup_fp ); /* アドバイス:RNNにおいては,ダイナミクス(中間層のニューロン数)は多いほど良い */ srnn = new SRNN(DIM_SIGNAL, 20, LEN_DATA_SEQUENCE, PREDICT_LENGTH, sample, sample_maxmin); // delete [] sample; // delete [] sample_maxmin; } // センサーのセットアップ. static void sensor_setup(void) { sensor_module = new SensorModule(5); } /* // ネットワークのセットアップ static void network_setup(void) { // セットアップ, 最初の時間取得 const char prefix_net_str[] = "[Network Status]"; //setup ethernet interface printf("%s Ethernet initializing....", prefix_net_str); if ( eth_if.init() < 0 ) {// Use DHCP fprintf( stderr, "%s Ethernet init failed. \r\n", prefix_net_str); exit(1); } if ( eth_if.connect() < 0 ) { // (offlineが確定する -> offline modeへ). fprintf( stderr, "%s Ethernet connect failed. \r\n", prefix_net_str); exit(1); } // init time printf("%s Trying to update time...\r\n", prefix_net_str); // Please specify near ntp server. ex) Japan -> ntp.nict.jp:123 if (ntp_client.setTime("ntp.nict.jp") == 0) { printf("%s Set time successfully! \r\n", prefix_net_str); } else { fprintf( stderr, "%s Error in setup time \r\n", prefix_net_str); } // setup http server // http_server = new HTTPServer(80, "/local/"); printf("%s IP Address : %s \r\n", prefix_net_str, eth_if.getIPAddress()); printf("%s Network setup finished! \r\n", prefix_net_str); } */ // グラフィックハンドラの初期化 static void graphic_handler_setup(void) { graphic_handler = new GraphicHandler(DIM_SIGNAL, PREDICT_INTERVAL_TIME, PREDICT_LENGTH, get_JST()); } // データの初期化(アロケート) static void data_setup(void) { new_seqence_data = new float[DIM_SIGNAL]; // 現在の(一番新しい)系列データ new_predict_data = new float[DIM_SIGNAL * PREDICT_LENGTH]; // 現在の予測結果 new_predict_weather = new int[PREDICT_LENGTH]; // 現在の予測天気 new_predict_probability = new float[PREDICT_LENGTH]; // 現在の予測天気の確率 } // セットアップ. void setup(void) { printf("SETUP START "); printf("-------------------------- \r\n"); mcsvm_setup(); printf("SVM ...OK \r\n"); srnn_setup(); printf("SRNN ...OK \r\n"); sensor_setup(); printf("SENSOR ...OK \r\n"); //network_setup(); set_time(time(NULL)); printf("NETWORK ...NO(offline) \r\n"); graphic_handler_setup(); printf("GRAPHIC ...OK \r\n"); data_setup(); printf("SHARED DATA ...OK \r\n"); printf("SETUP SUCCESS "); printf("-------------------------- \r\n"); }