def test_three_dim_polynomial(self):
class ThreeDimPolynomial(Function):
"""
2x^2 - y - 2y^2 + x = z
Minimum at (4x+1==0, 4y-1==0)
"""
def value_and_gradient(self, point):
x, y = point['x'], point['y']
value = 2*x**2 - y - 2*y**2 + x
gradient = Counter({'x' : 4*x+1, 'y' : 4*y-1})
return (value, gradient)
def value(self, point):
x, y = point['x'], point['y']
return 2*x**2 - y + 3*y**3 - 2*y**2 + x
threedimfunc = ThreeDimPolynomial()
start = Counter()
start['x'] = 0
start['y'] = 0
min_point = Minimizer.minimize(threedimfunc, start, quiet=True)
self.assertAlmostEqual(min_point['x'], -0.25, 3)
self.assertAlmostEqual(min_point['y'], 0.25, 3)
def test_three_dim_polynomial(self):
class ThreeDimPolynomial(Function):
"""
2x^2 - y - 2y^2 + x = z
Minimum at (4x+1==0, 4y-1==0)
"""
def value_and_gradient(self, point):
x, y = point['x'], point['y']
value = 2 * x**2 - y - 2 * y**2 + x
gradient = Counter({'x': 4 * x + 1, 'y': 4 * y - 1})
return (value, gradient)
def value(self, point):
x, y = point['x'], point['y']
return 2 * x**2 - y + 3 * y**3 - 2 * y**2 + x
threedimfunc = ThreeDimPolynomial()
start = Counter()
start['x'] = 0
start['y'] = 0
min_point = Minimizer.minimize(threedimfunc, start, quiet=True)
self.assertAlmostEqual(min_point['x'], -0.25, 3)
self.assertAlmostEqual(min_point['y'], 0.25, 3)
def train_with_features(self, labeled_features, sigma=None, quiet=False):
print "Optimizing weights..."
weight_function = MaxEntWeightFunction(labeled_features, self.labels, self.features)
weight_function.sigma = sigma
print "Building initial dictionary..."
initial_weights = CounterMap()
print "Training on %d labelled features" % (len(labeled_features))
print "Minimizing..."
self.weights = Minimizer.minimize(weight_function, initial_weights, quiet=quiet)
def train_with_features(self, labeled_features, sigma=None, quiet=False):
print "Optimizing weights..."
weight_function = MaxEntWeightFunction(labeled_features, self.labels,
self.features)
weight_function.sigma = sigma
print "Building initial dictionary..."
initial_weights = CounterMap()
print "Training on %d labelled features" % (len(labeled_features))
print "Minimizing..."
self.weights = Minimizer.minimize(weight_function,
initial_weights,
quiet=quiet)
def test_two_dim_polynomial(self): class TwoDimPolynomial(Function): """ 2x^2 - 10x + 27 Minimum at 4x-10 = 0: x = 2.5 """ def value_and_gradient(self, point): value = 2 * (point['y']-5)**2 + 2 # 2(x-5)^2+2 => 2x^2 - 10x + 27 gradient = Counter() gradient['y'] = 4 * point['y'] - 10 return (value, gradient) def value(self, point): return 2 * (point['y']-5)**2 + 2 Minimizer.max_iterations = 1000 twodimfunc = TwoDimPolynomial() start = Counter() start['y'] = 0.0 min_point = Minimizer.minimize(twodimfunc, start, quiet=True) self.assertAlmostEqual(min_point['y'], 2.5, 3)
def test_two_dim_polynomial(self):
class TwoDimPolynomial(Function):
"""
2x^2 - 10x + 27
Minimum at 4x-10 = 0: x = 2.5
"""
def value_and_gradient(self, point):
value = 2 * (point['y'] -
5)**2 + 2 # 2(x-5)^2+2 => 2x^2 - 10x + 27
gradient = Counter()
gradient['y'] = 4 * point['y'] - 10
return (value, gradient)
def value(self, point):
return 2 * (point['y'] - 5)**2 + 2
Minimizer.max_iterations = 1000
twodimfunc = TwoDimPolynomial()
start = Counter()
start['y'] = 0.0
min_point = Minimizer.minimize(twodimfunc, start, quiet=True)
self.assertAlmostEqual(min_point['y'], 2.5, 3)
