def main(resfile=None):
DATADIR = '../dataset'
def ftrain(name):
return path.join(DATADIR, '{}-train.txt'.format(name))
def ftest(name):
return path.join(DATADIR, '{}-test.txt'.format(name))
for fname in ['sonar', 'splice']:
traindataMat, traintagMat = loadData(ftrain(fname))
testdataMat, testtagMat = loadData(ftest(fname))
traindataMat, testdataMat = norm(traindataMat, testdataMat)
for dims in [10, 20, 30]:
timestamp = time.time()
projectionMat = pca(traindataMat, dims)
# 投影
traindataMat_proj = traindataMat * projectionMat
testdataMat_proj = testdataMat * projectionMat
accuracy = _1NN(traindataMat_proj, testdataMat_proj, traintagMat,
testtagMat)
print("(PCA on %s)当维度为%d时,正确率为：" % (fname, dims), accuracy)
print('time used:', time.time() - timestamp, 's')
if resfile != None:
resfile.write('PCA(norm), %s, k = %d, %.16f\n' %
(fname, dims, accuracy))
def main(resfile=None):
DATADIR = '../dataset'
def ftrain(name):
return path.join(DATADIR, '{}-train.txt'.format(name))
def ftest(name):
return path.join(DATADIR, '{}-test.txt'.format(name))
for fname, KNN_K in {'sonar': 6, 'splice': 4}.items():
traindataMat, traintagMat = loadData(ftrain(fname))
testdataMat, testtagMat = loadData(ftest(fname))
for dims in [10, 20, 30]:
timestamp = time.time()
dataMat_dimReduced = isomap(vstack([traindataMat, testdataMat]),
dims, KNN_K)
accuracy = _1NN(
dataMat_dimReduced[range(0, len(traindataMat)), :],
dataMat_dimReduced[
range(len(traindataMat), len(dataMat_dimReduced)), :],
mat(traintagMat), mat(testtagMat))
print("(ISOMAP on %s(%d-NN))当维度为%d时,正确率为：" % (fname, KNN_K, dims),
accuracy)
print("time used：", time.time() - timestamp, 's')
if resfile != None:
resfile.write('ISOMAP, %s(%d-NN), k = %d, %.16f\n' %
(fname, KNN_K, dims, accuracy))