def train(self,X,y,reg=1e-5,num_iters=400,norm=True):
"""
Train a linear model using scipy's function minimization.
Inputs:
- X: N X D array of training data. Each training point is a D-dimensional
row.
- y: 1-dimensional array of length N with values in the reals.
- reg: (float) regularization strength.
- num_iters: (integer) number of steps to take when optimizing
- norm: a boolean which indicates whether the X matrix is standardized before
solving the optimization problem
Outputs:
- optimal value for theta
"""
num_train,dim = X.shape
# standardize features if norm=True
if norm:
# take out the first column and do the feature normalize
X_without_1s = X[:,1:]
X_norm, mu, sigma = utils.std_features(X_without_1s)
# add the ones back
XX = np.vstack([np.ones((X_norm.shape[0],)),X_norm.T]).T
else:
XX = X
# initialize theta
theta = np.zeros((dim,))
# Run scipy's fmin algorithm to run gradient descent
theta_opt_norm = scipy.optimize.fmin_bfgs(self.loss, theta, fprime = self.grad_loss, args=(XX,y,reg),maxiter=num_iters)
if norm:
# convert theta back to work with original X
theta_opt = np.zeros(theta_opt_norm.shape)
theta_opt[1:] = theta_opt_norm[1:]/sigma
theta_opt[0] = theta_opt_norm[0] - np.dot(theta_opt_norm[1:],mu/sigma)
else:
theta_opt = theta_opt_norm
return theta_opt
def train(self, X, y, num_iters=400):
"""
Train a linear model using scipy's function minimization.
Inputs:
- X: N X D array of training data. Each training point is a D-dimensional
row.
- y: 1-dimensional array of length N with values in the reals.
- num_iters: (integer) number of steps to take when optimizing
Outputs:
- optimal value for theta
"""
num_train, dim = X.shape
# standardize X so that each column has zero mean and unit variance
# remember to take out the first column and do the feature normalize
X_without_1s = X[:, 1:]
X_norm, mu, sigma = utils.std_features(X_without_1s)
# add the ones back and assemble the XX matrix for training
XX = np.vstack([np.ones((X_norm.shape[0], )), X_norm.T]).T
theta = np.zeros((dim, ))
# Run scipy's fmin algorithm to run gradient descent
theta_opt_norm = scipy.optimize.fmin_bfgs(self.loss,
theta,
fprime=self.grad_loss,
args=(XX, y),
maxiter=num_iters)
# convert theta back to work with original X
theta_opt = np.zeros(theta_opt_norm.shape)
theta_opt[1:] = theta_opt_norm[1:] / sigma
theta_opt[0] = theta_opt_norm[0] - np.dot(theta_opt_norm[1:],
mu / sigma)
return theta_opt
def train(self,X,y,num_iters=400):
"""
Train a linear model using scipy's function minimization.
Inputs:
- X: N X D array of training data. Each training point is a D-dimensional
row.
- y: 1-dimensional array of length N with values in the reals.
- num_iters: (integer) number of steps to take when optimizing
Outputs:
- optimal value for theta
"""
num_train,dim = X.shape
# standardize X so that each column has zero mean and unit variance
# remember to take out the first column and do the feature normalize
X_without_1s = X[:,1:]
X_norm, mu, sigma = utils.std_features(X_without_1s)
# add the ones back and assemble the XX matrix for training
XX = np.vstack([np.ones((X_norm.shape[0],)),X_norm.T]).T
theta = np.zeros((dim,))
# Run scipy's fmin algorithm to run gradient descent
theta_opt_norm = scipy.optimize.fmin_bfgs(self.loss, theta, fprime = self.grad_loss, args=(XX,y),maxiter=num_iters)
# convert theta back to work with original X
theta_opt = np.zeros(theta_opt_norm.shape)
theta_opt[1:] = theta_opt_norm[1:]/sigma
theta_opt[0] = theta_opt_norm[0] - np.dot(theta_opt_norm[1:],mu/sigma)
return theta_opt
X_train.append(x)
if i % 5000 == 0:
print("Reading data for index = ", i)
X_train = np.asarray(X_train)
X_val = []
for i in range(10001, 11001):
file = "../train/" + str(i) + ".png"
x = si.imread(file)
x = x.reshape(3072)
X_val.append(x)
if i % 5000 == 0:
print("Reading data for index = ", i)
X_val = np.asarray(X_val)
X_train, _, _ = utils.std_features(X_train)
X_train = np.vstack((np.ones(X_train.shape[0]), X_train.T)).T
X_val, _, _ = utils.std_features(X_val)
X_val = np.vstack((np.ones(X_val.shape[0]), X_val.T)).T
theta = np.random.randn(3073, 10) * 0.0001
loss, grad = softmax_loss_naive(theta, X_train, y_train, 0.0)
# Loss should be something close to - log(0.1)
print 'loss:', loss, ' should be close to ', -np.log(0.1)
tic = time.time()
loss_naive, grad_naive = softmax_loss_naive(theta, X_train, y_train, 0.00001)
toc = time.time()
import scipy.io
import utils
import numpy as np
from sklearn import linear_model
# No modifications in this script
# complete the functions in util.py; then run the script
# load the spam data in
Xtrain,Xtest,ytrain,ytest = utils.load_spam_data()
# Preprocess the data
Xtrain_std,mu,sigma = utils.std_features(Xtrain)
Xtrain_logt = utils.log_features(Xtrain)
Xtrain_bin = utils.bin_features(Xtrain)
Xtest_std = (Xtest - mu)/sigma
Xtest_logt = utils.log_features(Xtest)
Xtest_bin = utils.bin_features(Xtest)
# find good lambda by cross validation for these three sets
def run_dataset(X,ytrain,Xt,ytest,type,penalty):
best_lambda = utils.select_lambda_crossval(X,ytrain,0.1,5.1,0.5,penalty)
print "best_lambda = ", best_lambda
# train a classifier on best_lambda and run it
if penalty == "l2":
if i % 5000 == 0:
print("Reading data for index = ", i)
X_train = np.asarray(X_train)
X_val = []
for i in range(10001,11001):
file = "../train/"+str(i)+".png"
x = si.imread(file)
x = x.reshape(3072)
X_val.append(x)
if i % 5000 == 0:
print("Reading data for index = ", i)
X_val = np.asarray(X_val)
X_train,_,_ = utils.std_features(X_train)
X_train = np.vstack((np.ones(X_train.shape[0]), X_train.T)).T
X_val,_,_ = utils.std_features(X_val)
X_val = np.vstack((np.ones(X_val.shape[0]), X_val.T)).T
theta = np.random.randn(3073,10) * 0.0001
loss, grad = softmax_loss_naive(theta, X_train, y_train, 0.0)
# Loss should be something close to - log(0.1)
print 'loss:', loss, ' should be close to ', - np.log(0.1)
tic = time.time()
loss_naive, grad_naive = softmax_loss_naive(theta, X_train, y_train, 0.00001)
toc = time.time()
