return model.predict(x)
if __name__=='__main__':
vari,deli,m = 'cu','1712',7
db = DB()
days = db.execute("select date from contract_daily where vari=%s and deli=%s order by date asc",
(vari, deli))[30:]
data = DataSet(m, 'direction')
model = NaiveBayes(m)
y_real = []
y_pred = []
for day in days:
model.fit(*data.bootstrap(vari, deli, day, 300))
r = model.predict(data.get_x(vari, deli, day))
if r[0]>=r[1] and r[0]>=r[2]:
r = 0
elif r[1]>=r[0] and r[1]>=r[2]:
r = 1
else:
r = 2
y_pred.append(r)
y_real.append(data.get_y(vari, deli, day))
print (accuracy_score(y_real, y_pred))
print (y_real)
print (y_pred)
for j in range(self.m):
self.likelihood_tensor[y, j, x[j]] += 1
for y in range(self.y_classes):
for x_dim in range(self.m):
self.likelihood_tensor[y, x_dim] = [(self.likelihood_tensor[y,x_dim,x]+self.laplace)/(self.prior[y]+self.x_classes*self.laplace) for x in range(self.x_classes)]
self.prior = np.array([(x+self.laplace)/(n+self.y_classes*self.laplace) for x in self.prior])
def predict(self, x):
P = [1.] * self.y_classes
for i in range(self.y_classes):
P[i] = self.prior[i]
for j in range(self.m):
P[i] *= self.likelihood(i, j, x[j])
s = sum(P)
P = [1.*x/s for x in P]
res = {}
for i in range(self.y_classes):
res[i] = P[i]
return res
if __name__=='__main__':
from dataSet import DataSet
import datetime
data = DataSet(5, 'direction')
model = NaiveBayes(5)
X,Y = data.bootstrap('cu', '1707', datetime.date(2017,6,6), n=300)
model.fit(X, Y)
print ('predict:', model.predict(data.get_x('cu', '1707', datetime.date(2017,6,6))))
print ('real:', data.get_y('cu', '1707', datetime.date(2017,6,6)))