def main(testfile=None):
if testfile:
tr = rd.read(testfile)
ti = np.array(tr)
tir = [np.reshape(x, (784, 1)) for x in ti]
return (n.evaluate(tir))
else:
n.SGD(a, 20, 20, 3, 0, c)
def main(testfile=None):
if testfile:
tf=rd.read(testfile)
ts=np.array(tf)
return(clf.predict(ts))
else:
pred=[int(a) for a in clf.predict(c[0])]
nc=sum(int(x==y) for x,y in zip(pred,c[1]))
print "{0} of {1} correct = {2}%".format(nc,len(c[1]),(100.0*float(nc)/len(c[1])))
def main(testfile=None): if testfile: a=rd.read(testfile) k=3 predictions=[] for x in range(len(a)): neighbors = knn.getNeighbors(train, a[x], k) result = knn.getResponse(neighbors) predictions.append(result) return(predictions) else: knn.main(train,tests)
def main(testfile=None):
global n
global a
global b
if testfile:
tr = rd.read(testfile)
ti = np.array(tr)
tir = [np.reshape(x, (2500, 1)) for x in ti]
res = n.evaluate(tir)
for i in res:
res_converted.append(cnv.convert(int(i)))
print(res_converted)
else:
n.SGD(a, 20, 20, 3.0, b)
def main(testfile=None):
global clf
global a
global b
if testfile:
tf=rd.read(testfile)
ts=np.array(tf)
res=clf.predict(ts)
for i in res:
res_converted.append(cnv.convert(int(i)))
print (res_converted)
else:
pred=[int(a+.1) for a in clf.predict(b[0])]
nc=sum(int(x==y) for x,y in zip(pred,b[1]))
print "{0} of {1} correct = {2}%".format(nc,len(b[1]),(100.0*float(nc)/len(b[1])))