def train_bydate(cnt=100): json = f.get_json_data("ml_train_tf") span = json["span"] term_len = json["term_len"] dup_len = json["dup_len"] train_startd = span[0] train_endd = span[1] starti = 0 eigyobis = dtf.get_eigyobis(train_startd, train_endd) while starti+term_len <= len(eigyobis): tmp_endd = eigyobis[starti+term_len] f.log("Training nikkei 225") train225(eigyobis[starti], tmp_endd) f.log("Training from %s to %s" % (eigyobis[starti], tmp_endd)) multi_training(eigyobis[starti], tmp_endd, get_meigaras(cnt, tmp_endd)) predict_starti = starti+term_len+1 if predict_starti < len(eigyobis): predict_endi = predict_starti + term_len if predict_endi >= len(eigyobis): predict_endi = len(eigyobis)-1 tmp_endd = eigyobis[predict_endi] f.log("Making prediction from %s to %s" % (eigyobis[predict_starti], tmp_endd)) multi_predict2db(eigyobis[starti], tmp_endd, get_meigaras(cnt, tmp_endd, term_len)) starti += term_len - dup_len f.log("Finished")
def train_bydate(cnt=100, startd="", endd="", term_len=0, dup_len=0): json = f.get_json_data("ml_train_tf") span = json["span"] if term_len == 0: term_len = json["term_len"] if dup_len == 0: dup_len = json["dup_len"] if startd == "": startd = span[0] if endd == "": endd = span[1] eigyobis = dtf.get_eigyobis(startd, endd) starti = 0 tfl = TfLearning(restore_first=True) while starti+term_len <= len(eigyobis): tmp_endd = eigyobis[starti+term_len] f.log("Training from %s to %s" % (eigyobis[starti], tmp_endd)) tfl.run(eigyobis[starti], tmp_endd, tmp_endd, get_meigaras(cnt, tmp_endd)) starti += term_len - dup_len