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# EFILTER Forensic Query Language
#
# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
(EXPERIMENTAL) EFILTER stdlib - math module.
"""
__author__ = "Adam Sindelar <adamsh@google.com>"
import six
from six.moves import xrange
from efilter.protocols import counted
from efilter.protocols import number
from efilter.stdlib import core
# Analytical functions:
class LevenshteinDistance(core.TypedFunction):
"""Compute Levenshtein distance between 'x' and 'y'.
Levenshtein distance is, informally, the number of insert/delete/substitute
operations needed to transform 'x' to 'y'. Computing the distance takes
O(N * M) steps using the bottom-up dynamic programming approach below.
See: https://en.wikipedia.org/wiki/Levenshtein_distance.
"""
name = "levenshtein"
def __call__(self, x, y):
lx = len(x)
ly = len(y)
# Base cases:
if not lx:
return ly
if not ly:
return lx
if lx > ly:
# This saves space, because the rows are shorter.
return self(y, x)
# Conceptually, this is a matrix of edit distances between prefixes of
# x and y, arranged so that every coordinate pair into the matrix is
# the levenshtein distance between the first 'i' characters of 'x' and
# first 'j' characters of 'y'. To compute the distance from x to y we
# need all intermediate results, but only the last two rows at a time.
# The first row of edit distances: an empty string can be transformed
# into a string of length N in N steps.
current_row = list(xrange(lx))
for i in xrange(1, ly):
previous_row = current_row
current_row = [0] * lx
current_row[0] = i
for j in xrange(1, lx):
if x[j - 1] == y[i - 1]:
substitution_cost = 0
else:
substitution_cost = 1
# One of three operations will have to lowest cost. They are,
# in order, substitution (or nop), deletion and insertion.
current_row[j] = min(
previous_row[j - 1] + substitution_cost,
previous_row[j] + 1,
current_row[j - 1] + 1)
return current_row[-1]
@classmethod
def reflect_static_args(cls):
return (six.string_types[0], six.string_types[0])
@classmethod
def reflect_static_return(cls):
return int
# Aggregate functions (reducers):
class Mean(core.TypedReducer):
"""(EXPERIMENTAL) Computes the mean."""
name = "mean"
def fold(self, chunk):
return (sum(chunk), counted.count(chunk))
def merge(self, left, right):
return (left[0] + right[0], left[1] + right[1])
def finalize(self, intermediate):
total, count = intermediate
return float(total) / count
@classmethod
def reflect_static_return(cls):
return int
class Sum(core.TypedReducer):
"""(EXPERIMENTAL) Computes a sum of numbers."""
name = "sum"
def fold(self, chunk):
return sum(chunk)
def merge(self, left, right):
return left + right
def finalize(self, intermediate):
return intermediate
@classmethod
def reflect_static_return(cls):
return number.INumber
class VectorSum(core.TypedReducer):
"""(EXPERIMENTAL) Computes a sum of vectors of numbers of constant size."""
name = "vector_sum"
def fold(self, chunk):
iterator = iter(chunk)
running_sum = list(next(chunk))
expected_len = len(running_sum)
for row in iterator:
if len(row) != expected_len:
raise ValueError(
"vector_sum can only add up vectors of same size.")
for idx, col in enumerate(row):
running_sum[idx] += col
def merge(self, left, right):
return self.fold([left, right])
def finalize(self, intermediate):
return intermediate
@classmethod
def reflect_static_return(cls):
return list
MODULE = core.LibraryModule(
name="stdmath",
vars={
Mean.name: Mean(),
Sum.name: Sum(),
LevenshteinDistance.name: LevenshteinDistance()
}
)
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