Weather casting with Machine Learning (SVM and SRNN).
Dependencies: EthernetInterface GraphicHandler NTPClient SRNN SVM SensorModule mbed-rtos mbed
main.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 "main.hpp" LocalFileSystem *local_fs; // マウントポイントを定義(ディレクトリパスになる) // Pointer to Class instance (global) SRNN *srnn; MCSVM *mcsvm; SensorModule *sensor_module; GraphicHandler *graphic_handler; // ネットワーク関係(global) EthernetInterface eth_if; NTPClient ntp_client; // 系列データ float* new_seqence_data; // 現在の(一番新しい)系列データ float* new_predict_data; // 現在の予測結果 int* new_predict_weather; // 現在の予測天気 float* new_predict_probability; // 現在の予測天気の確率(厳密には,確率ではない...) FILE* seqence_data_fp; // 系列データのファイルポインタ(SRNNと計器の記録に使う) FILE* predict_data_fp; // 予測データ volatile int ml_flag; time_t now_time; struct tm* local_time_p; // 生存報告LED DigitalOut live_led(LED1); volatile int thread_count = 0; int open_count = 0; // 計器タスク void read_task(void const *arg) { // char str_buf[BUF_SIZE]; // データ更新 // __disable_irq(); // 割り込み禁止 sensor_module->read_all_sensor(); new_seqence_data[TEMPERATURE] = sensor_module->get_temperture(); new_seqence_data[AIR_PRESSURE] = sensor_module->get_pressure(); new_seqence_data[HUMIDITY] = sensor_module->get_humidity(); printf("T:%f P:%f H:%f \r\n", new_seqence_data[TEMPERATURE], new_seqence_data[AIR_PRESSURE], new_seqence_data[HUMIDITY]); graphic_handler->set_now_data(new_seqence_data); // __enable_irq(); // 割り込み許可 /* sprintf( str_buf, "%d/%d/%d %d:%d:%d,%.2f,%.2f,%.2f\n", (local_time_p->tm_year + 1900), (local_time_p->tm_mon + 1), local_time_p->tm_mday, local_time_p->tm_hour, local_time_p->tm_min, local_time_p->tm_sec, new_seqence_data[TEMPERATURE], new_seqence_data[AIR_PRESSURE], new_seqence_data[HUMIDITY]); */ seqence_data_fp = fopen( SEQUENCE_DATA_NAME, "a"); // check_file_open( seqence_data_fp, SEQUENCE_DATA_NAME); // 形式に沿った文字列を書き出す : y/m/d h:m,<temperature>,<air_pressure>,<humidity> fprintf( seqence_data_fp, "%d/%d/%d %d:%d:%d,%f,%f,%f\n", (local_time_p->tm_year + 1900), (local_time_p->tm_mon + 1), local_time_p->tm_mday, local_time_p->tm_hour, local_time_p->tm_min, local_time_p->tm_sec, new_seqence_data[TEMPERATURE], new_seqence_data[AIR_PRESSURE], new_seqence_data[HUMIDITY]); // fputs( str_buf, seqence_data_fp ); fclose( seqence_data_fp ); } // 機械学習タスク void ml_task(void const *arg) { // ローカル変数 int line = 0, ret; //float* srnn_sample = new float[LEN_DATA_SEQUENCE * DIM_SIGNAL]; // SRNNのサンプル float srnn_sample[LEN_DATA_SEQUENCE * DIM_SIGNAL]; // 読み込みバッファ float buf_data[DIM_SIGNAL]; char str_buf[BUF_SIZE], str_dummy[50], str_dum1[10], str_dum2[10], str_dum3[10]; printf("[%d] M.L.S.T.A.R.T \r\n", thread_count++); // 3. SRNNに学習データを読み込ませる. printf("Set SRNN sample... %02d:%02d \r\n", local_time_p->tm_hour, local_time_p->tm_min); seqence_data_fp = fopen( SEQUENCE_DATA_NAME, "r"); check_file_open( seqence_data_fp, SEQUENCE_DATA_NAME); // まず、行数を数える while( fgets( str_buf, seqence_data_fp) != NULL ) { } line = 0; while( ( ret = fscanf( seqence_data_fp, " %[^\n,],%f,%f,%f", str_buf, &(buf_data[0]), &(buf_data[1]), &(buf_data[2])) ) != EOF ) { if (line == LEN_DATA_SEQUENCE) break; memcpy(&(srnn_sample[line * DIM_SIGNAL]), buf_data, sizeof(float) * DIM_SIGNAL); printf("sample %d : %s %f %f %f \r\n", line, str_buf, MATRIX_AT(srnn_sample,DIM_SIGNAL,line,0), MATRIX_AT(srnn_sample,DIM_SIGNAL,line,1), MATRIX_AT(srnn_sample,DIM_SIGNAL,line,2)); line++; } /* while( fgets( str_buf, BUF_SIZE, seqence_data_fp) != NULL ) { if (line == LEN_DATA_SEQUENCE) break; printf("%s \r", str_buf); //sscanf( str_buf, " %[^\n,],%f,%f,%f", str_buf, &(buf_data[0]), &(buf_data[1]), &(buf_data[2])) ) sscanf( str_buf, "%[^,],%[^,],%[^,],%[^,]", str_dummy , str_dum1 , str_dum2 , str_dum3); srnn_sample[line * DIM_SIGNAL] = float(atof(str_dum1)); srnn_sample[line * DIM_SIGNAL + 1] = float(atof(str_dum2)); srnn_sample[line * DIM_SIGNAL + 2] = float(atof(str_dum3)); printf(" str : %s , f0 : %f , f1 : %f , f2 : %f \r\n", str_dummy , srnn_sample[line * DIM_SIGNAL] , srnn_sample[line * DIM_SIGNAL + 1] , srnn_sample[line * DIM_SIGNAL + 2]); // memcpy(&(srnn_sample[line * DIM_SIGNAL]), buf_data, sizeof(float) * DIM_SIGNAL); line++; } */ fclose( seqence_data_fp ); srnn->set_sample(srnn_sample); // 4. SRNNの学習/予測結果から, MCSVMで天気識別 printf("Learning... %02d:%02d \r\n", local_time_p->tm_hour, local_time_p->tm_min); srnn->learning(); srnn->predict(new_seqence_data); // 金曜日ここから memcpy(new_predict_data, srnn->predict_signal, sizeof(float) * DIM_SIGNAL * PREDICT_LENGTH); // MCSVMによる天候識別 for (int i_predict = 0; i_predict < PREDICT_LENGTH; i_predict++) { // printf("predict_data[%d] : %f %f %f \r\n", i_predict, new_predict_data[i_predict * DIM_SIGNAL], new_predict_data[i_predict * DIM_SIGNAL + 1], new_predict_data[i_predict * DIM_SIGNAL + 2]); new_predict_weather[i_predict] = mcsvm->predict_label(&(new_predict_data[i_predict * DIM_SIGNAL])); new_predict_probability[i_predict] = mcsvm->predict_probability(&(new_predict_data[i_predict * DIM_SIGNAL])); // printf("P_W : %d P_P : %f \r\n", new_predict_weather[i_predict], new_predict_probability[i_predict]); } printf("SVM predict finished \r\n"); // 5. 予測結果の書き込み printf("Write out predict... %02d:%02d \r\n", local_time_p->tm_hour, local_time_p->tm_min); predict_data_fp = fopen( PREDICT_DATA_NAME, "w"); check_file_open( predict_data_fp, PREDICT_DATA_NAME); for (int i_predict = 0; i_predict < PREDICT_LENGTH; i_predict++) { // 予測時刻へ変換 now_time += PREDICT_INTERVAL_TIME; local_time_p = localtime(&now_time); // 気象を文字列に変換 switch(new_predict_weather[i_predict]) { case SHINY: strcpy(str_buf, "shiny"); break; case CLOUDY: strcpy(str_buf, "cloudy"); break; case RAINY: strcpy(str_buf, "rainy"); break; case SNOWY: strcpy(str_buf, "snowy"); break; default: fprintf( stderr, "Error in write predict result (in weather switch). \r\n"); break; } // 書き出しフォーマット : y/m/d h:m,<weather>,<temperature>,<air_pressure>,<humidity> fprintf( predict_data_fp, "%d/%d/%d %d:%d:%d,%s,%f,%f,%f\n", (local_time_p->tm_year + 1900), (local_time_p->tm_mon + 1), local_time_p->tm_mday, local_time_p->tm_hour, local_time_p->tm_min, local_time_p->tm_sec, str_buf, new_predict_data[i_predict * DIM_SIGNAL + TEMPERATURE], new_predict_data[i_predict * DIM_SIGNAL + AIR_PRESSURE], new_predict_data[i_predict * DIM_SIGNAL + HUMIDITY]); } fclose( predict_data_fp ); // GraphicHandlerの現在予測データのセット graphic_handler->set_predict_data(new_predict_data, new_predict_weather, new_predict_probability); // delete [] srnn_sample; // delete local_time_p; <- してはいけない(戒め) } // 描画スレッド : 優先度低め void draw_task(void const *arg) { while (true) { if (ml_flag) { Thread::signal_wait(0x3, osWaitForever); } // 1. 描画更新 <- 学習中は止めたい... printf("draw thread start. \r\n"); if (time(NULL) % 60 == 0) { // 一分毎に表示時間を更新 graphic_handler->update_time(); } graphic_handler->update_image(); graphic_handler->update_draw(); printf("draw thread finish. \r\n"); Thread::wait(1 * 1000); } } // ネットワークスレッド : リソースの限界. 廃止. /* void network_task(void const *arg) { while (true) { while (ml_flag) { Thread::signal_wait(0x3); } // 1. ポート80のListen http_server->poll(); // for (int dum = 0; dum < 10000; dum++) ; net_led = !net_led; // Thread::wait(500); } } */ // 生存報告LEDチカ void liveled_task(void const *arg) { while (true) { if (ml_flag) { Thread::signal_wait(0x1, osWaitForever); } live_led = !live_led; Thread::wait(1000); } } // int main(void) { set_new_handler(no_memory); local_fs = new LocalFileSystem("local"); setup(); ml_flag = 1; Thread draw_thread(draw_task, NULL, osPriorityNormal, 2000); Thread liveled_thread(liveled_task, NULL, osPriorityLow, 200); // Thread read_thread(read_task, NULL, osPriorityNormal); osThreadSetPriority(Thread::gettid() ,osPriorityHigh); while (true) { Thread::wait(4 * 1000); now_time = get_JST(); local_time_p = localtime(&now_time); read_task(NULL); // truncate_data_file(); ml_task(NULL); ml_flag = 0; liveled_thread.signal_set(0x1); draw_thread.signal_set(0x3); } printf("finished."); return 1; }