def main():
sd = SynthData()
ca = coordinateAscent()
cal = CoordinateAscentLasso()
st = Standardize()
beta0Seperate = True
lam = 0.1
X, y, b = sd.generateData(noise=False,
w=np.array([1, 1, 1, 1, 1, 0, 0, 0, 0,
0])[np.newaxis].T)
#if beta0Seperate:
# beta = np.array([1, 1, 1, 1, 0, 0, 0, 0])[np.newaxis].T
#else:
# beta = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0])[np.newaxis].T
#if beta0Seperate:
# y = 1 + np.dot(X, beta)
#else:
# X = np.append(np.ones((X.shape[0], 1)), X, 1)
# y = np.dot(X, beta)
print('Fitting the model with Lasso:')
print('Lambda = ' + str(lam))
print('beta0, array of betas:')
t0 = time()
print(cal.coordinateAscentLasso(y, X, lam, [], False, beta0Seperate))
dt = time() - t0
print('done in %.4fs.' % dt)
print()
print('Fitting the model with plain \'ol Coordinate Ascent')
print('beta0, array of betas:')
t0 = time()
print(ca.coordinateAscent(y, X, [], False))
dt = time() - t0
print('done in %.4fs.' % dt)
print()
#print('Dictionary Learning')
#dl = decomposition.DictionaryLearning(fit_algorithm='cd')
#print(dl.fit(X))
#print(np.shape(dl.components_))
#print(dl.components_)
print('Fitting the model with LARS (from the scikit library)')
clf = linear_model.LassoLars(alpha=0.01)
t0 = time()
print(clf.fit(X, y))
dt = time() - t0
print('done in %.4fs.' % dt)
print('array of betas:')
print(clf.coef_)
return 1
def main():
sd = SynthData()
ca = coordinateAscent()
cal = CoordinateAscentLasso()
st = Standardize()
beta0Seperate = True
lam = 0.1
X, y, b = sd.generateData(noise=False, w=np.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0])[np.newaxis].T)
#if beta0Seperate:
# beta = np.array([1, 1, 1, 1, 0, 0, 0, 0])[np.newaxis].T
#else:
# beta = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0])[np.newaxis].T
#if beta0Seperate:
# y = 1 + np.dot(X, beta)
#else:
# X = np.append(np.ones((X.shape[0], 1)), X, 1)
# y = np.dot(X, beta)
print('Fitting the model with Lasso:')
print('Lambda = ' + str(lam))
print('beta0, array of betas:')
t0 = time()
print(cal.coordinateAscentLasso(y, X, lam, [], False, beta0Seperate))
dt = time() - t0
print('done in %.4fs.' % dt)
print()
print('Fitting the model with plain \'ol Coordinate Ascent')
print('beta0, array of betas:')
t0 = time()
print(ca.coordinateAscent(y, X, [], False))
dt = time() - t0
print('done in %.4fs.' % dt)
print()
#print('Dictionary Learning')
#dl = decomposition.DictionaryLearning(fit_algorithm='cd')
#print(dl.fit(X))
#print(np.shape(dl.components_))
#print(dl.components_)
print('Fitting the model with LARS (from the scikit library)')
clf = linear_model.LassoLars(alpha=0.01)
t0 = time()
print(clf.fit(X, y))
dt = time() - t0
print('done in %.4fs.' % dt)
print('array of betas:')
print(clf.coef_)
return 1
def dictLearning(self):
# p > n?
n = 5
p = 10
r = 5
# Dictionary - Line generates a random normal distributed Matrix with dimensions of R^n*p
# will be x in lasso
X = np.random.random((n, p))
self.X = X
# same as D = np.random.randn(s, r)
# sparse code R^p*1 with # of zeros < p (or n?), rest are ones
w = np.zeros((p, r))
for i in range(r):
w[: np.random.randint(1, n), i] = 1
np.random.shuffle(w[:, i])
self.w = w
print(w)
# print(np.dot(w, w.T))
# Data matrix - R^n*1
# will be y in Lasso
self.y = np.dot(X, w) + 0.1 * np.random.randn(n, r)
y = self.y
print(y)
print("X = ")
print(np.shape(X))
print("m*k")
print("w = ")
print(np.shape(w))
print("k*n")
print("y = ")
print(np.shape(y))
print("m*n")
# assume default tolerance and number of iterations
TOL = 1e-6
MAXIT = 100
A = 0
B = 0
# Coordinate Ascent Lasso
cal = CoordinateAscentLasso()
for i in range(MAXIT):
# each column of weights 'corresponds' to to columns of y.
for k in range(r):
# updating the weights column-wise
w[:, k] = cal.coordinateAscentLasso(y[:, k][np.newaxis].T, X, 0.1)[1].flatten()
# print('w updated using Lasso: ')
# print(w)
# A = A + np.dot(w, w.T)
# B = B + np.dot(y, w.T)
# print('A')
# print(A)
# exit()
# X = self.updateDict(X, A, B)
X = self.updateDict5(X, y, w)
# X = self.updateDict3(X, y, w)
# print()
# print(X)
# print(np.shape(X))
# return 1
# select complete k-th column, select complete k-th row
# X[:, k], w[k, :]
# print(y - np.dot(X, w))
# print(np.shape(y - np.dot(X, w)))
# print(np.shape(w))
# print(np.dot(y - np.dot(X, w), w.T))
# X = np.divide(np.dot(y - np.dot(X, w), w.T).T, w).T
# print(np.shape(X))
return w, X
def dictLearning(self):
#p > n?
n = 5
p = 10
r = 5
#Dictionary - Line generates a random normal distributed Matrix with dimensions of R^n*p
#will be x in lasso
X = np.random.random((n, p))
self.X = X
#same as D = np.random.randn(s, r)
#sparse code R^p*1 with # of zeros < p (or n?), rest are ones
w = np.zeros((p, r))
for i in range(r):
w[:np.random.randint(1, n), i] = 1
np.random.shuffle(w[:, i])
self.w = w
print(w)
#print(np.dot(w, w.T))
#Data matrix - R^n*1
#will be y in Lasso
self.y = np.dot(X, w) + 0.1 * np.random.randn(n, r)
y = self.y
print(y)
print('X = ')
print(np.shape(X))
print('m*k')
print('w = ')
print(np.shape(w))
print('k*n')
print('y = ')
print(np.shape(y))
print('m*n')
#assume default tolerance and number of iterations
TOL = 1e-6
MAXIT = 100
A = 0
B = 0
#Coordinate Ascent Lasso
cal = CoordinateAscentLasso()
for i in range(MAXIT):
#each column of weights 'corresponds' to to columns of y.
for k in range(r):
#updating the weights column-wise
w[:, k] = cal.coordinateAscentLasso(y[:, k][np.newaxis].T, X,
0.1)[1].flatten()
#print('w updated using Lasso: ')
#print(w)
#A = A + np.dot(w, w.T)
#B = B + np.dot(y, w.T)
#print('A')
#print(A)
#exit()
#X = self.updateDict(X, A, B)
X = self.updateDict5(X, y, w)
#X = self.updateDict3(X, y, w)
#print()
#print(X)
#print(np.shape(X))
#return 1
#select complete k-th column, select complete k-th row
#X[:, k], w[k, :]
#print(y - np.dot(X, w))
#print(np.shape(y - np.dot(X, w)))
#print(np.shape(w))
#print(np.dot(y - np.dot(X, w), w.T))
#X = np.divide(np.dot(y - np.dot(X, w), w.T).T, w).T
#print(np.shape(X))
return w, X
