def test_code():
fail = 0
fail_list = np.array([])
for i in range(1000):
# create two learners and get data
s = np.random.randint(0, 1489683274)
#print(s)
#s = 993360834
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4LinReg(s)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4LinReg() results"
print "RMSE LR : ", rmseLR
print "RMSE DT : ", rmseDT
if rmseLR < 0.9 * rmseDT:
print "LR < 0.9 DT: pass"
else:
print "LR >= 0.9 DT: fail"
fail = fail + 1
fail_list = np.append(fail_list, s)
print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4DT(s)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4RT() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseDT
if rmseDT < 0.9 * rmseLR:
print "DT < 0.9 LR: pass"
else:
print "DT >= 0.9 LR: fail"
fail = fail + 1
fail_list = np.append(fail_list, s)
print
print(fail, 'You failed')
print(fail_list, 'Failed seeds')
def test_code(seedval=1489683273):
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose = False)
dtlearner = dt.DTLearner(verbose = False, leaf_size = 1)
X, Y = best4LinReg(seed=seedval)
dtX = X
dtY = Y
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4LinReg() results"
print "RMSE LR : ", rmseLR
print "RMSE DT : ", rmseDT
if rmseLR < 0.9 * rmseDT:
print "LR < 0.9 DT: pass"
else:
print "LR >= 0.9 DT: fail"
print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose = False)
dtlearner = dt.DTLearner(verbose = False, leaf_size = 1)
X, Y = best4DT(seed=seedval)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4RT() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseDT
if rmseDT < 0.9 * rmseLR:
print "DT < 0.9 LR: pass"
else:
print "DT >= 0.9 LR: fail"
print
return dtX, dtY, X, Y
def test_code():
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose=False)
rtlearner = rt.RTLearner(verbose=False, leaf_size=1)
X, Y = best4LinReg()
numPass = 0
numFail = 0
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# share results
print
print "best4LinReg() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseRT
if rmseLR < 0.9 * rmseRT:
numPass = numPass + 1
print "LR < 0.9 RT: pass"
else:
numFail = numFail + 1
print "LR < 0.9 RT: fail"
print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
rtlearner = rt.RTLearner(verbose=False, leaf_size=1)
X, Y = best4RT()
numPass = 0
numFail = 0
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# share results
print
print "best4RT() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseRT
if rmseRT < 0.9 * rmseLR:
numPass = numPass + 1
print "RT < 0.9 LR: pass"
else:
numFail = numFail + 1
print "RT < 0.9 LR: fail"
print
def test_code():
fail1, fail2 = 0, 0
for i in range(500):
R = int(1000 * np.random.random_sample())
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4LinReg(R)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4LinReg() results"
print "RMSE LR : ", rmseLR
print "RMSE DT : ", rmseDT
if rmseLR < 0.9 * rmseDT:
print "LR < 0.9 DT: pass"
else:
fail1 += 1
print "LR >= 0.9 DT: fail"
print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4DT(R)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4RT() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseDT
if rmseDT < 0.9 * rmseLR:
print "DT < 0.9 LR: pass"
else:
fail2 += 1
print "DT >= 0.9 LR: fail"
print
print "fail1: %i" % fail1
print "fail2: %i" % fail2
def test_code():
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose=False)
rtlearner = rt.RTLearner(verbose=False, leaf_size=1)
X, Y = best4LinReg()
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# share results
print
print "best4LinReg() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseRT
if rmseLR < 0.9 * rmseRT:
print "LR < 0.9 RT: pass"
else:
print "LR < 0.9 RT: fail"
print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
rtlearner = rt.RTLearner(verbose=False, leaf_size=1)
X, Y = best4RT(seed=5)
# data = np.genfromtxt('../mc3_p1/Data/ripple.csv', delimiter = ',');
## data = data[1:,1:]
# X = data[:,:-1]; Y = data[:,-1]
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.scatter(X[:,0], X[:,1], Y, c='c')
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# share results
print
print "best4RT() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseRT
if rmseRT < 0.9 * rmseLR:
print "RT < 0.9 LR: pass"
else:
print "RT < 0.9 LR: fail"
print
def test_code():
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose=False)
rtlearner = rt.RTLearner(verbose=False, leaf_size=1)
numPass = 0
numFail = 0
for i in range(1000):
X, Y = best4LinReg(1)
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# share results
if rmseLR < 0.9 * rmseRT:
numPass = numPass + 1
else:
numFail = numFail + 1
print
print "Number of Pass: "******"Number of Fail: " + str(numFail)
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
rtlearner = rt.RTLearner(verbose=False, leaf_size=1)
numPass = 0
numFail = 0
for i in range(1000):
X, Y = best4RT(1)
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# share results
if rmseRT < 0.9 * rmseLR:
numPass = numPass + 1
else:
numFail = numFail + 1
print
print "Number of Pass: "******"Number of Fail: " + str(numFail)
def test_code():
i = list(range(0, 16))
for trial in i:
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4LinReg()
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4LinReg() results"
print "RMSE LR : ", rmseLR
print "RMSE DT : ", rmseDT
if rmseLR < 0.9 * rmseDT:
print "LRL test number: ", trial, "\nLR < 0.9 DT: pass"
else:
print "LRL test number: ", trial, "\nLR >= 0.9 DT: fail"
print
i = list(range(1, 16))
for trial in i:
# get data that is best for a decision tree
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4DT()
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4RT() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseDT
if rmseDT < 0.9 * rmseLR:
print "DT test number: ", trial, "\nDT < 0.9 LR: pass"
else:
print "DT test number: ", trial, "\nDT >= 0.9 LR: fail"
print
def test_code():
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose=False)
rtlearner = rt.RTLearner(verbose=False, leaf_size=1)
X, Y = best4LinReg()
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# share results
print
print "best4LinReg() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseRT
if rmseLR < 0.9 * rmseRT:
print "LR < 0.9 RT: pass"
else:
print "LR < 0.9 RT: fail"
print
# share results
for i in range(2000):
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
rtlearner = rt.RTLearner(verbose=False, leaf_size=1)
X, Y = best4RT()
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# print
# print "best4RT() results"
# print "RMSE LR : ", rmseLR
# print "RMSE RT : ", rmseRT
if rmseRT < 0.9 * rmseLR:
# print "RT < 0.9 LR: pass"
continue
else:
print i
# print "RT < 0.9 LR: fail"
# print
print "Done"
def test_code():
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
#X, Y = best4LinReg()
X, Y = best4LinReg(seed=100, n_samples=100, n_features=5)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4LinReg() results"
print "RMSE LR : ", rmseLR
print "RMSE DT : ", rmseDT
if rmseLR < 0.9 * rmseDT:
print "LR < 0.9 DT: pass"
else:
print "LR >= 0.9 DT: fail"
print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
#X, Y = best4DT()
X, Y = best4DT(seed=10, n_samples=1000, n_features=10)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print
print "best4DT() results"
print "RMSE LR : ", rmseLR
print "RMSE DT : ", rmseDT
if rmseDT < 0.9 * rmseLR:
print "DT < 0.9 LR: pass"
else:
print "DT >= 0.9 LR: fail"
print
def test_code():
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4LinReg(seed=1)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print()
print("best4LinReg() results")
print(f"RMSE LR : {rmseLR}")
print(f"RMSE DT : {rmseDT}")
print(f"ratio LR/DT : {rmseLR/rmseDT}")
if rmseLR < 0.9 * rmseDT:
print("LR < 0.9 DT: pass")
else:
print("LR >= 0.9 DT: fail")
print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4DT(seed=1010)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
print()
print("best4RT() results")
print(f"RMSE LR : {rmseLR}")
print(f"RMSE DT : {rmseDT}")
print(f"ratio DT/LR : {rmseDT/rmseLR}")
if rmseDT < 0.9 * rmseLR:
print("DT < 0.9 LR: pass")
else:
print("DT >= 0.9 LR: fail")
print
def test_code():
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose = False)
rtlearner = rt.RTLearner(verbose = False, leaf_size = 1)
X, Y = best4LinReg()
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# share results
print
print "best4LinReg() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseRT
if rmseLR < 0.9 * rmseRT:
print "LR < 0.9 RT: pass"
else:
print "LR < 0.9 RT: fail"
print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose = False)
rtlearner = rt.RTLearner(verbose = False, leaf_size = 1)
X, Y = best4RT()
# compare the two learners
rmseLR, rmseRT = compare_os_rmse(lrlearner, rtlearner, X, Y)
# share results
print
print "best4RT() results"
print "RMSE LR : ", rmseLR
print "RMSE RT : ", rmseRT
if rmseRT < 0.9 * rmseLR:
print "RT < 0.9 LR: pass"
else:
print "RT < 0.9 LR: fail"
print
def test_code():
# create two learners and get data
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4LinReg(int(np.mod(time.time() * 10000, 10000)))
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
# print
# print "best4LinReg() results"
# print "RMSE LR : ", rmseLR
# print "RMSE DT : ", rmseDT
if rmseLR < 0.9 * rmseDT:
print '.',
else:
print "LR >= 0.9 DT: fail", 'LR RMSE=', rmseLR, 'DT RMSE=', rmseDT
# print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4DT(int(np.mod(time.time() * 10000, 10000)))
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
# print
# print "best4DT() results"
# print "RMSE LR : ", rmseLR
# print "RMSE DT : ", rmseDT
if rmseDT < 0.9 * rmseLR:
print '.',
else:
print "DT >= 0.9 LR: fail", 'DT RMSE=', rmseDT, 'LR RMSE=', rmseLR
def test_code():
# create two learners and get data
tot = 0
m = np.random.randint(100000000, size=10000)
for i in range(0, m.size):
r = np.random.randint(1000000000)
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(leaf_size=1, verbose=False)
X, Y = best4LinReg(seed=r)
#np.savetxt('test.csv', X, delimiter=',')
#np.savetxt('testy.csv', Y.astype(int), delimiter=',')
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
# share results
if rmseLR >= 0.9 * rmseDT:
print "LR < 0.9 DT: fail"
tot += 1
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4DT(seed=r)
# compare the two learners
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
if rmseDT >= 0.9 * rmseLR:
print "DT >= 0.9 LR: fail"
tot += 1
print(tot)
import numpy as np import gen_data import math print np.random.randint(0, 2 * math.pi, size=(100, 2)) X, Y = gen_data.best4DT(123) X2, Y2 = gen_data.best4DT(123) XL1, YL1 = gen_data.best4LinReg(123) XL2, YL2 = gen_data.best4LinReg(123) print X print Y print np.array_equal(X, X2) print np.array_equal(Y, Y2) print np.array_equal(XL1, XL2) print np.array_equal(YL1, YL2) print X.shape print Y.shape print np.random.randint(0, 2 * math.pi, size=(100, 2))
