| 1) Create Word Embedding using Glove | |
| model_glove = word2vec.Word2Vec(word2vec_sentences, size=200, window=10, sg=1, hs=0, min_count=1, negative=10, workers=-1, iter=5) | |
| 2)from sklearn.cluster import AgglomerativeClustering | |
| ----------------to form cluster use this ------------------ | |
| In [37]: | |
| wv_clusters = AgglomerativeClustering(n_clusters=50, affinity="cosine", linkage="average") | |
| In [38]: | |
| wv_clusters.fit(model_label_specific.syn0norm) |
| from __future__ import division | |
| import operator | |
| from pyspark import SparkContext | |
| from pyspark.sql import SQLContext | |
| from pyspark.sql.functions import col | |
| import numpy as np | |
| import networkx as nx | |
| import matplotlib.pyplot as plt | |
| sc = SparkContext("local", "Decision Tree") |
| #%matplotlib notebook | |
| %matplotlib inline | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from matplotlib import colors as c | |
| import time | |
| fig = plt.figure() | |
| ax = fig.gca() |
Sublime Text includes a command line tool, subl, to work with files on the command line. This can be used to open files and projects in Sublime Text, as well working as an EDITOR for unix tools, such as git and subversion.
Applications folderSetup
Sublime Text includes a command line tool, subl, to work with files on the command line. This can be used to open files and projects in Sublime Text, as well working as an EDITOR for unix tools, such as git and subversion.
Applications folderSetup
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| { | |
| "embeddings": [ | |
| { | |
| "tensorName": "My tensor", | |
| "tensorShape": [ | |
| 1000, | |
| 50 | |
| ], | |
| "tensorPath":"https://gist.githubusercontent.com/bhushanbrb/952a29e2cb046ae7b74e9ad7d2ca2972/raw/a1f528d5da44352a794cd8af373e72bb4d64188c/pred.tsv", | |
| "metadataPath":"https://gist.githubusercontent.com/bhushanbrb/1c50fcf13f0eb034c1cb28b1607939ab/raw/573f15f926b672c3eb2cde849f02d3665a8d2be6/omsid.tsv" |
| { | |
| "embeddings": [ | |
| { | |
| "tensorName": "Vector Search with pictures and queries", | |
| "tensorShape": [ | |
| 3000, | |
| 128 | |
| ], | |
| "tensorPath": "https://gist.githubusercontent.com/MariaFjodorowa/68277095197a798f8aa02c59f35df234/raw/28d4fda62828833834c59f19910f4c980e822d23/vecs_queries_prod.tsv", | |
| "metadataPath": "https://gist.githubusercontent.com/MariaFjodorowa/f9db18927d1b6f45bf9d1e61ff56fa4b/raw/19025a9223c41e45ccebb35e82ab59ed2e7b1fc4/meta_queries_prod.tsv", |