def problem_1c(n=500):
algorithmList = [
'Perceptron', 'Perceptron with margin', 'Winnow', 'Winnow with margin',
'AdaGrad'
]
bestParaList = {500: [(1, 0), (0.03, 1), (1.1, 0), (1.1, 2.0), (0.25, 1)], \
1000: [(1, 0), (0.03, 1), (1.1, 0), (1.1, 2.0), (0.25, 1)]}
t = Trainer()
d = t.data_generator(l=10,
m=100,
n=n,
number_of_instances=50000,
noise=False)
x, y = d['x'], d['y']
initDict = initDictGenerator(n=t.n)
color = 'rgbyk'
for idx in range(len(algorithmList)):
algorithm = algorithmList[idx]
algorithmInit = initDict[algorithm]
if n in bestParaList:
lr, mg = bestParaList[n][idx]
else:
lr, mg = bestParaList[500][idx]
algorithmInit['learning rate'] = [lr]
algorithmInit['margin'] = [mg]
t.learning(algorithm,
x,
y,
initDict={algorithm: algorithmInit},
times=1)
t._unpack_resDict(lr, mg)
plt.plot(t.mistake_list, color[idx])
plt.legend(algorithmList, loc='best')
plt.title('Plots for n = %s' % n)
plt.show()
def problem_1b(n=500):
# change n for two size of datasets
algorithmList = [
'Perceptron', 'Perceptron with margin', 'Winnow', 'Winnow with margin',
'AdaGrad'
]
t = Trainer()
d = t.data_generator(l=10,
m=100,
n=n,
number_of_instances=50000,
noise=False)
x, y = d['x'], d['y']
D1_x, D1_y, D2_x, D2_y = d['D1_x'], d['D1_y'], d['D2_x'], d['D2_y']
initDict = initDictGenerator(n=t.n)
for algorithm in algorithmList:
algorithmInit = initDict[algorithm]
learningRateList = algorithmInit['learning rate']
marginList = algorithmInit['margin']
t.learning(algorithm, D1_x, D1_y, initDict=initDict, times=20)
for lr in learningRateList:
for mg in marginList:
err_rate = t.error_estimate(t.D2_x, t.D2_y, lr, mg)
mistake = t.mistakeCount(lr, mg)
print(
'LR: {0: >6s}, MG: {1: >6s}, ER: {2: >6s}, Mis: {3: >6s}'.
format(str(lr), str(mg), str(err_rate), str(mistake)))
def problem_2_plot():
algorithmList = [
'Perceptron', 'Perceptron with margin', 'Winnow', 'Winnow with margin',
'AdaGrad'
]
bestParaList = {
40: [(1, 0), (0.25, 1), (1.1, 0), (1.1, 2.0), (1.5, 1)],
80: [(1, 0), (0.03, 1), (1.1, 0), (1.1, 2.0), (0.25, 1)],
120: [(1, 0), (0.03, 1), (1.1, 0), (1.1, 2.0), (0.25, 1)],
160: [(1, 0), (0.03, 1), (1.1, 0), (1.1, 2.0), (0.25, 1)],
200: [(1, 0), (0.25, 1), (1.1, 0), (1.1, 2.0), (1.5, 1)]
}
record = {}
for algorithm in algorithmList:
record[algorithm] = []
for n in range(40, 240, 40):
print()
t = Trainer()
d = t.data_generator(l=10,
m=20,
n=n,
number_of_instances=50000,
noise=False)
x, y = d['x'], d['y']
initDict = initDictGenerator(n=t.n)
color = 'rgbyk'
for idx in range(len(algorithmList)):
algorithm = algorithmList[idx]
algorithmInit = initDict[algorithm]
if n in bestParaList:
lr, mg = bestParaList[n][idx]
else:
lr, mg = bestParaList[500][idx]
algorithmInit['learning rate'] = [lr]
algorithmInit['margin'] = [mg]
t.learningWithStop(algorithm,
x,
y,
initDict={algorithm: algorithmInit},
times=1)
print(len(t.resDict[lr, mg][3]))
t._unpack_resDict(lr, mg)
record[algorithm].append(t.mistake_list[-1])
i = 0
for algorithm in algorithmList:
plt.plot(range(40, 240, 40), record[algorithm], color[i])
i += 1
plt.legend(algorithmList, loc='best')
plt.xlabel('Number of total features')
plt.xticks(range(40, 240, 40))
plt.ylabel('Total Mistakes before stop')
#plt.title('Plots for n = %s'%n)
plt.savefig('p2plot.png', dpi=144)
plt.show()
def problem_3_dataGenerator():
l = 10
n = 1000
for m in [100, 500, 1000]:
t = Trainer()
train = t.data_generator(l=l,
m=m,
n=n,
number_of_instances=50000,
noise=True)
test = t.data_generator(l=l,
m=m,
n=n,
number_of_instances=10000,
noise=False)
np.save('p3trainX_m=%s' % m, train['x'])
np.save('p3trainY_m=%s' % m, train['y'])
np.save('p3testX_m=%s' % m, test['x'])
np.save('p3testY_m=%s' % m, test['y'])
def problem_3_pureDataGenerator():
l = 10
n = 1000
for m in [100, 500, 1000]:
t = Trainer()
train = t.data_generator(l=l,
m=m,
n=n,
number_of_instances=50000,
noise=False)
np.save('p3pureX_m=%s' % m, train['x'])
np.save('p3pureY_m=%s' % m, train['y'])
def problem_4():
i = 0
t = Trainer()
l, m, n = 10, 20, 40
l1 = []
l2 = []
color = 'rgbymc'
lr, mg = (0.25, 1)
initDict = initDictGenerator(n=n)
algorithm = 'AdaGrad'
algorithmInit = initDict[algorithm]
algorithmInit['learning rate'] = [lr]
algorithmInit['margin'] = [mg]
for j in range(50):
d = t.data_generator(l=l,
m=m,
n=n,
number_of_instances=10000,
noise=True)
x, y = d['x'], d['y']
t.learningHingeLoss(algorithm,
x,
y,
initDict={algorithm: algorithmInit},
times=1)
res = t.resDict[(lr, mg)]
w, theta = res[0], res[1]
hinge, mis = t.hinge_and_mis(x, y, w, theta)
l1.append(hinge)
l2.append(mis)
plt.plot(range(1, 51), l1, color[i])
plt.plot(range(1, 51), l2, color[i + 1])
plt.xlabel('Datasets')
plt.ylabel('Total value of Hinge Loss / Misclassification Loss')
plt.yticks(range(0, 3000, 300))
plt.legend(['Hinge Loss', 'Misclassification Loss'])
plt.savefig('p4plot.png', dpi=144)
plt.show()