$kubectl get pods -o wide
$kubectl get pods
$kubectl get rc
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| import scala.util.hashing.MurmurHash3 | |
| def md3(s: String) = { | |
| MurmurHash3.stringHash(s).toString | |
| } | |
| val hasehd = List(md3("1"), md3("2"), md3("3"), md3("Product:12345"), md3("Product:12346"), md3("Product:12347")) | |
| val unhashed = List("1", "2", "Product:12345", "Product:12346", "Product:12347", "3") |
Renien
/ n-gram-scala.scala
Created
May 25, 2016 15:52
In the fields of computational linguistics and probability, an n-gram is a contiguous sequence of n items from a given sequence of text or speech.
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| object NGram { | |
| /** | |
| * Split the sentence | |
| * @param data documents | |
| * @param splitter the delimiting regular expression | |
| * @return the array of strings computed by splitting this string | |
| * around matches of the given regular expression | |
| */ | |
| private def split(data: String, splitter: String): Seq[String] ={ |
Renien
/ a-priori-python.py
Created
May 15, 2016 13:40
We can find all frequent pairs by making two passes over the baskets. On the first pass, we count the items themselves, and then determine which items are frequent. On the second pass, we count only the pairs of items both of which are found frequently on the first pass. Monotonicity justifies our ignoring other pairs.
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| __author__ = 'renienj' | |
| import urllib | |
| from itertools import combinations | |
| import time | |
| from collections import defaultdict | |
| """ | |
| Sample Data Sets | |
| ---------------- |
Renien
/ k-shingling-python.py
Created
May 14, 2016 14:10
A k-shingle is any k characters that appear consecutively in a document. If we represent a document by its set of k-shingles, then the Jaccard similarity of the shingle sets measures the textual similarity of documents. Sometimes, it is useful to hash shingles to bit strings of shorter length, and use sets of hash values to represent documents.
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| __author__ = 'renienj' | |
| def compute_gram(doc_data, k=2): | |
| """ | |
| In natural language processing a w-shingling is a set of unique "shingles" | |
| (n-grams, contiguous subsequences of tokens in a document) | |
| Very much similar to n-grams but here we consider characters | |
| """ |
Renien
/ tf-ids-python.py
Last active
May 14, 2016 13:11
The measure called TF.IDF lets us identify words in a collection of documents that are useful for determining the topic of each document. A word has high TF.IDF score in a document if it appears in relatively few documents, but appears in this one, and when it appears in a document it tends to appear many times.
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| __author__ = 'renienj' | |
| import numpy as np | |
| import pandas as pd | |
| import math as math | |
| def compute_tfidf(tf_list, idf_list): | |
| """ | |
| tfidf = tf(w) * idf(w) |
Renien
/ jaccard-similarity-python.py
Last active
May 27, 2021 19:31
Jaccard Similarity: The Jaccard similarity of sets is the ratio of the size of the intersection of the sets to the size of the union. This measure of similarity is suitable for many applications, including textual similarity of documents and similarity of buying habits of customers.
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| __author__ = 'renienj' | |
| import numpy as np | |
| def compute_jaccard_similarity_score(x, y): | |
| """ | |
| Jaccard Similarity J (A,B) = | Intersection (A,B) | / | |
| | Union (A,B) | | |
| """ | |
| intersection_cardinality = len(set(x).intersection(set(y))) |
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| <!doctype html> | |
| <html> | |
| <head> | |
| <title>Network | Basic usage</title> | |
| <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.0.0-beta1/jquery.min.js"></script> | |
| <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/vis/4.15.0/vis.js"></script> | |
| <script type="text/javascript" src="pos-pattern.js"></script> | |
| <link href="https://cdnjs.cloudflare.com/ajax/libs/vis/4.15.0/vis.css" rel="stylesheet" type="text/css" /> |
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| __author__ = 'renienj' | |
| #!/usr/bin/env python | |
| import sys | |
| def read_input(file): | |
| for line in file: | |
| # split the line into words | |
| yield line.split() |