def test_dilation(self):
self.m12 = DenseMatrix(np.mat([[3,1],[9,2]]))
self.m22 = DenseMatrix(np.mat([[4,3],[2,1]]))
self.ph2 = DenseMatrix(np.mat([[18,11],[24,7]]))
self.row = ["a", "b"]
self.ft = ["f1","f2"]
self.space1 = Space(DenseMatrix(self.m12), self.row, self.ft)
self.space2 = Space(DenseMatrix(self.ph2), ["a_a","a_b"], self.ft)
m = Dilation()
m.export(self.prefix + ".dil1")
m.train([("a","b","a_b")], self.space1, self.space2)
m.export(self.prefix + ".dil2")
def test_dilation(self):
self.m12 = DenseMatrix(np.mat([[3, 1], [9, 2]]))
self.m22 = DenseMatrix(np.mat([[4, 3], [2, 1]]))
self.ph2 = DenseMatrix(np.mat([[18, 11], [24, 7]]))
self.row = ["a", "b"]
self.ft = ["f1", "f2"]
self.space1 = Space(DenseMatrix(self.m12), self.row, self.ft)
self.space2 = Space(DenseMatrix(self.ph2), ["a_a", "a_b"], self.ft)
m = Dilation()
m.export(self.prefix + ".dil1")
m.train([("a", "b", "a_b")], self.space1, self.space2)
m.export(self.prefix + ".dil2")
#-------
from composes.utils import io_utils
from composes.composition.dilation import Dilation
#training data
train_data = [("good", "car", "good_car"),
("good", "book", "good_book")
]
#load an argument space
arg_space = io_utils.load("./data/out/ex10.pkl")
#load a phrase space
phrase_space = io_utils.load("data/out/PHRASE_SS.ex10.pkl")
print "Training phrase space"
print phrase_space.id2row
print phrase_space.cooccurrence_matrix
#train a Dilation model on the data
my_comp = Dilation()
my_comp.train(train_data, arg_space, phrase_space)
#print its parameters
print "\nlambda:", my_comp._lambda
#use the model to compose the train data
composed_space = my_comp.compose([("good", "bike", "good_bike")],
arg_space)
print "\nComposed space:"
print composed_space.id2row
print composed_space.cooccurrence_matrix
#ex14.py
#-------
from composes.utils import io_utils
from composes.composition.dilation import Dilation
#training data
train_data = [("good", "car", "good_car"), ("good", "book", "good_book")]
#load an argument space
arg_space = io_utils.load("./data/out/ex10.pkl")
#load a phrase space
phrase_space = io_utils.load("data/out/PHRASE_SS.ex10.pkl")
print "Training phrase space"
print phrase_space.id2row
print phrase_space.cooccurrence_matrix
#train a Dilation model on the data
my_comp = Dilation()
my_comp.train(train_data, arg_space, phrase_space)
#print its parameters
print "\nlambda:", my_comp._lambda
#use the model to compose the train data
composed_space = my_comp.compose([("good", "bike", "good_bike")], arg_space)
print "\nComposed space:"
print composed_space.id2row
print composed_space.cooccurrence_matrix
