def regression(X, Y, X_out, Y_out, l, ts='linear'):
X = transform(X, ts)
X_out = transform(X_out, ts)
w = weightDecayRegression((X, Y), l)
# print w
return classificationError((X, Y), w), classificationError((X_out, Y_out),
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 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 regression(X, Y, X_out, Y_out, l, ts='linear'): X = transform(X, ts) X_out = transform(X_out, ts) w = weightDecayRegression((X, Y), l) # print w return classificationError((X, Y), w), classificationError((X_out, Y_out), w)
