def test_regression(w, test, ds): # get data data = fl.format_inputs(test, ds) # predict results m = rg.iter2matrix(data) x = m.T[:-1].T x = rg.append_ones(x) y = m.T[-1:].T # test error return rg.mse_error(x, y, w)
def train_grad_descent(training, params): data,ds = transform_train_set(training) # generate regression m = rg.iter2matrix(data) x = m.T[:-1].T x = rg.append_ones(x) y = m.T[-1:].T init_weight = np.ones((x.shape[1],1)) w = gd.grad_descent(x,y,init_weight,params["alpha"],params["iterations"]) # get training error terror = rg.mse_error(x,y,w) return (w, ds, terror)
def train_regression(training, params={}):
# get data
data,ds = transform_train_set(training)
# generate regression
m = rg.iter2matrix(data)
x = m.T[:-1].T
x = rg.append_ones(x)
y = m.T[-1:].T
w = rg.regression_weights(x,y)
# get training error
terror = rg.mse_error(x,y,w)
return (w, ds, terror)
def train_lasso(training, params={}):
# get data
# t = fl.list2ordered_dict(training)
# data,ds = fl.get_data2(t)
data,ds = transform_train_set(training)
# generate regression
m = rg.iter2matrix(data)
x = m.T[:-1].T
y = m.T[-1:].T
w = rl.lasso_reg(x,y)
# get training error
x = rg.append_ones(x)
terror = rg.mse_error(x,y,w)
return (w, ds, terror)
import unicodecsv as csv
import regression as rg
dataf = open('data/features/feature_data.csv', 'rt')
datac = csv.reader(dataf)
next(datac)
m = rg.iter2matrix(datac)
x = m.T[:-1].T
x = rg.append_ones(x)
y = m.T[-1:].T
init_weight = np.ones((x.shape[1], 1))
final_weight = grad_descent(x, y, init_weight, 0.00004, 1000)
print final_weight
print rg.mse_error(x, y, final_weight)
import unicodecsv as csv
import numpy as np
import scipy as sp
import math
import regression as rg
dataf = open('data/features/feature_data.csv', 'rt')
datac = csv.reader(dataf)
next(datac)
m = rg.iter2matrix(datac)
x1 = m.T[:-1].T
x = rg.append_ones(x1)
y = m.T[-1:].T
w = rg.regression_weights(x,y)
lse = rg.lse_error(x, y, w)
mse = rg.mse_error(x, y, w)
print "weights: " + str(w)
print "lse: " + str(lse)
print "mse: " + str(mse)
import unicodecsv as csv
import regression as rg
dataf = open("data/features/feature_data.csv", "rt")
datac = csv.reader(dataf)
next(datac)
m = rg.iter2matrix(datac)
x = m.T[:-1].T
x = rg.append_ones(x)
y = m.T[-1:].T
init_weight = np.ones((x.shape[1], 1))
final_weight = grad_descent(x, y, init_weight, 0.00004, 1000)
print final_weight
print rg.mse_error(x, y, final_weight)
