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
