(800, 28)\
]
TIMES = 100
SHORTCARD = 256
SPEED = 0.01
DROP_INPUT = 0.2
DROP_HIDDEN = 0.6
PRINT_FROM = 10
PRINT_TO = 50
STEP_SHOW = 3
RHO = 0.9
EPSILON = 1e-4
STEP = 50
print "test"
trX, teX = load.loadMel("afterLDA100.db")
# trY, teY = load.loadRes()
trZ, teZ = load.loadLeter()
print "loaded"
neur = Neural()
# neur.load_from_file(WEIGHTS_FILE)
neur.init_model(ARR_SHAPE)
neur.train(trX, teX, trZ, teZ, epochs=TIMES)
neur.save_to_file(WEIGHTS_FILE)
# trT = neur.result(trX)
# teT = neur.result(teX)
# print "start train"
# neur2 = Neural()
import time startt = time.time() import numpy as np import ujson import load from sklearn.discriminant_analysis import LinearDiscriminantAnalysis print "test" DIM = 100 mel = load.loadMel()[0] res_class = load.loadClass()[0] print "loaded" # print res[0] # def myfunc(a): # print a # return a.tolist().index(1) def save_to_file(X, filename='afterLDA'): with open(filename + str(DIM) + ".db", 'w') as f: ujson.dump(X.tolist(), f) # vfunc = np.vectorize(myfunc) # res_class = vfunc(res) clf = LinearDiscriminantAnalysis(n_components=DIM) print "train" clf.fit(mel, res_class) print "trained" print clf.predict(mel[:10]) pred = clf.predict(mel) print res_class[:10]
import time
startt = time.time()
import numpy as np
import ujson
import load
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
print "test"
DIM = 100
mel = load.loadMel()[0]
res_class = load.loadClass()[0]
print "loaded"
# print res[0]
# def myfunc(a):
# print a
# return a.tolist().index(1)
def save_to_file(X, filename='afterLDA'):
with open(filename + str(DIM) + ".db", 'w') as f:
ujson.dump(X.tolist(), f)
# vfunc = np.vectorize(myfunc)
# res_class = vfunc(res)
clf = LinearDiscriminantAnalysis(n_components=DIM)
print "train"
clf.fit(mel, res_class)
print "trained"
print clf.predict(mel[:10])