def get_dataset_from_file(filename):
with open(filename) as datafile:
words = [line.strip().split('\t') for line in datafile]
dataset = [ [float(cell) for cell in row[:-1]] for row in words]
labels = map(comb(itemgetter(-1), float), words)
return dataset, labels
def load_dataset(filename='test_set.dataset'):
with open(filename) as data_file:
strs = map(comb(str.strip, str.split), data_file.readlines())
labels = map(comb(itemgetter(2), int), strs)
dataset = [[1.0, float(item[0]), float(item[1])] for item in strs]
return dataset, labels
def get_x_y(point_group):
# return point_group
return map(comb(itemgetter(0), itemgetter(1) ), point_group),\
map(comb(itemgetter(0), itemgetter(2) ), point_group)
from numpy import *
def get_dataset_from_file(filename):
with open(filename) as datafile:
words = [line.strip().split('\t') for line in datafile]
dataset = [ [float(cell) for cell in row[:-1]] for row in words]
labels = map(comb(itemgetter(-1), float), words)
return dataset, labels
def dataset_to_mat(dataset_and_labels, labels=None):
if labels:
return mat(dataset_and_labels), mat(labels).T
return mat(dataset_and_labels[0]), mat(dataset_and_labels[1]).T
get_matrix_from_file = comb(get_dataset_from_file, dataset_to_mat)
def standard_regress(x_matrix,y_matrix):
# x_matrix = mat(x_list)
# y_matrix = mat(y_list).T
x_t_x = x_matrix.T*x_matrix
if linalg.det(x_t_x) == 0.0:
raise "This matrix is singular, cannot do inverse"
# I is inverse, equal exp -1 to a matrix
return x_t_x.I * (x_matrix.T*y_matrix)
def draw_points(x_list, y_list, weight_matrix):
import matplotlib.pyplot as plt
x_matrix = mat(x_list)
y_matrix = mat(y_list)
