+++ /dev/null
-# This file is part of NIT ( http://www.nitlanguage.org ).
-#
-# 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.
-
-# NLPVector Space Model.
-#
-# The Vector Space Model (VSM) is used to compare natural language texts.
-# Texts are translated to multidimensionnal vectors then compared by cosine
-# similarity.
-module vsm
-
-import counter
-
-# A multi-dimensional vector.
-class NLPVector
- super Counter[String]
-
- # Cosine similarity of `self` and `other`.
- #
- # Gives the proximity in the range `[0.0 .. 1.0]` where 0.0 means that the
- # two vectors are orthogonal and 1.0 means that they are identical.
- #
- # ~~~
- # var v1 = new NLPVector
- # v1["x"] = 1
- # v1["y"] = 2
- # v1["z"] = 3
- #
- # var v2 = new NLPVector
- # v2["x"] = 1
- # v2["y"] = 2
- # v2["z"] = 3
- #
- # var v3 = new NLPVector
- # v3["a"] = 1
- # v3["b"] = 2
- # v3["c"] = 3
- #
- # print v1.cosine_similarity(v2)
- # #assert v1.cosine_similarity(v2) == 1.0
- # print v1.cosine_similarity(v3)
- # assert v1.cosine_similarity(v3) == 0.0
- # ~~~
- fun cosine_similarity(other: SELF): Float do
- # Collect terms
- var terms = new HashSet[String]
- for k in self.keys do terms.add k
- for k in other.keys do terms.add k
-
- # Get dot product of two verctors
- var dot = 0
- for term in terms do
- dot += self.get_or_default(term, 0) * other.get_or_default(term, 0)
- end
-
- return dot.to_f / (self.norm * other.norm)
- end
-
- # The norm of the vector.
- #
- # `||x|| = (x1 ** 2 ... + xn ** 2).sqrt`
- #
- # ~~~
- # var v = new NLPVector
- # v["x"] = 1
- # v["y"] = 1
- # v["z"] = 1
- # v["t"] = 1
- # assert v.norm.is_approx(2.0, 0.001)
- #
- # v["x"] = 1
- # v["y"] = 2
- # v["z"] = 3
- # v["t"] = 0
- # assert v.norm.is_approx(3.742, 0.001)
- # ~~~
- fun norm: Float do
- var sum = 0
- for v in self.values do sum += v ** 2
- return sum.to_f.sqrt
- end
-end