Entity embeddings are used to map categorial variables into eucledian space. Works particularly well with features with high cardinality. i.e the categorial variable would be having a lot of discrete values.
INPUT- TABULAR DATA -> ||| ALGORITHM ||| -> OUTPUT - ENTITY EMBEDDINGS
Algorithm -
| #!/usr/bin/env python | |
| import numpy as np | |
| from pylab import * | |
| import scipy.integrate as spi | |
| #Parameter Values | |
| PopIn= {'g1s_young':1/12,'g1i_young':1/12,'g1r_young':1/12,'g1s_mid':1/12,'g1i_mid':1/12,'g1r_mid':1/12,'g1s_old':1/12,'g1i_old':1/12,'g1r_old':1/12,'g1s_vold':1/12,'g1i_vold':1/12,'g1r_vold':1/12} | |
| arr = [1/12]*12 | |
| beta= 0.50 |
| import tensorflow as tf | |
| import numpy as np | |
| corpus_raw = 'He is the king . The king is royal . She is the royal queen ' | |
| # convert to lower case | |
| corpus_raw = corpus_raw.lower() | |
| words = [] | |
| for word in corpus_raw.split(): |
| import tensorflow as tf | |
| with tf.Session() as sess: | |
| """ | |
| idx (h,r) top_3 | |
| [ [ | |
| [0,1], [0,8,3], | |
| [1,3], [7,2,1], | |
| [2,4], [4,3,9], | |
| ] ] |
| Search PubMed for articles in the PMC Open Access Subset for which free full text (i.e. XML) is available: | |
| http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&retmode=xml&term=pubmed+pmc+open+access%5Bfilter%5D+AND+free+full+text%5Bfilter%5D | |
| Find the PMCID for an article by PMID: | |
| http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&db=pmc&retmode=xml&linkname=pubmed_pmc&id=24191168 | |
| Fetch full text article XML from PMC by PMCID: | |
| http://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=3804407 | |
| A PMC article for which the full text XML is not available: |