def problem1():
name = ['c://users//mark//in.dta', 'c://users//mark//out.dta']
in_data = dataSet(readIn(name[0]))
out_data = dataSet(readIn(name[1]))
X, Y = in_data
print 'Problem 1, 2'
for k in (3, 4, 5, 6, 7):
X_t, Y_t = X[:25, :k+1], Y[:25]
X_v, Y_v = X[25:, :k+1], Y[25:]
w = regression((X_t, Y_t))
print k, classificationError((X_v, Y_v), w), classificationError((out_data[0][:, :k+1], out_data[1]) , w)
print 'Problem 3, 4'
for k in (3, 4, 5, 6, 7):
X_t, Y_t = X[:25, :k+1], Y[:25]
X_v, Y_v = X[25:, :k+1], Y[25:]
w = regression((X_v, Y_v))
print k, classificationError((X_t, Y_t), w), classificationError((out_data[0][:, :k+1], out_data[1]) , w)
def problem1():
name = ['c://users//mark//in.dta', 'c://users//mark//out.dta']
in_data = dataSet(readIn(name[0]))
out_data = dataSet(readIn(name[1]))
X, Y = in_data
print 'Problem 1, 2'
for k in (3, 4, 5, 6, 7):
X_t, Y_t = X[:25, :k + 1], Y[:25]
X_v, Y_v = X[25:, :k + 1], Y[25:]
w = regression((X_t, Y_t))
print k, classificationError((X_v, Y_v), w), classificationError(
(out_data[0][:, :k + 1], out_data[1]), w)
print 'Problem 3, 4'
for k in (3, 4, 5, 6, 7):
X_t, Y_t = X[:25, :k + 1], Y[:25]
X_v, Y_v = X[25:, :k + 1], Y[25:]
w = regression((X_v, Y_v))
print k, classificationError((X_t, Y_t), w), classificationError(
(out_data[0][:, :k + 1], out_data[1]), w)
def problem7():
RO = [(3**.5 + 4)**.5,
(3**.5 - 1)**.5,
(9 + 4 * 6 **.5)**.5,
(9 - 6 **.5)**.5,
]
for i, ro in enumerate(RO):
points = [(-1.,0.), (ro, 1.), (1., 0.)]
points = np.array(points)
e0, e1 = 0, 0
for row in range(3):
val = points[row]
train = points[points[:,0] != val[0],:]
val = np.insert(val, 0, 1, 0)
train = np.insert(train, 0, 1, 1)
train0 = np.copy(train)
train0[:,1] = 0
w0 = regression(train0)
w1 = regression(train)
e0 += (val[:-1].dot(w0) - val[-1])**2
e1 += (val[:-1].dot(w1) - val[-1])**2
print i, ro, e0, e1
def problem7():
RO = [
(3**.5 + 4)**.5,
(3**.5 - 1)**.5,
(9 + 4 * 6**.5)**.5,
(9 - 6**.5)**.5,
]
for i, ro in enumerate(RO):
points = [(-1., 0.), (ro, 1.), (1., 0.)]
points = np.array(points)
e0, e1 = 0, 0
for row in range(3):
val = points[row]
train = points[points[:, 0] != val[0], :]
val = np.insert(val, 0, 1, 0)
train = np.insert(train, 0, 1, 1)
train0 = np.copy(train)
train0[:, 1] = 0
w0 = regression(train0)
w1 = regression(train)
e0 += (val[:-1].dot(w0) - val[-1])**2
e1 += (val[:-1].dot(w1) - val[-1])**2
print i, ro, e0, e1
