def estimateGrad(fun, x, delta):
""" Given a real-valued function fun, estimate its gradient numerically.
"""
grad = SparseVector({})
for key in x:
e = SparseVector({})
e[key] = 1.0
grad[key] = (fun(x + delta * e) - fun(x)) / delta
return grad
def test_gradTotalLoss(self):
rdd = self.sc.parallelize([
(SparseVector.SparseVector({'a': -1, 'b': -1}), -1),
(SparseVector.SparseVector({'a': -1, 'b': -1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1)
])
beta = SparseVector.SparseVector({'a':2, 'b':3})
kek = ParallelLogisticRegression.gradTotalLossRDD(rdd, beta)
def test_test(self):
data = [
(SparseVector.SparseVector({'a': -1, 'b': -1}), -1),
(SparseVector.SparseVector({'a': -1, 'b': -1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1)
]
beta = SparseVector.SparseVector({'a': 2, 'b': 2})
scores = LogisticRegression.test(data, beta)
def test_test(self):
rdd = self.sc.parallelize([
(SparseVector.SparseVector({'a': -1, 'b': -1}), -1),
(SparseVector.SparseVector({'a': -1, 'b': -1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1),
(SparseVector.SparseVector({'a':1, 'b': 1}), 1)
])
beta = SparseVector.SparseVector({'a':2, 'b':3})
kek = ParallelLogisticRegression.test(rdd, beta)
print kek
def setUp(self):
self.x = SparseVector.SparseVector(dict(a=1,b=1))
self.y = 1
self.beta = SparseVector.SparseVector(dict(a=3,b=3))
def test_p_norm(self):
sparse = SparseVector.SparseVector({'a': 2, 'b': 2})
def add(self, word, dimension, freq=1):
if not self.has_key(word):
self[word] = SparseVector.SparseVector()
self[word].add(dimension, freq)
