I hereby claim:
- I am amn41 on github.
- I am alanmnichol (https://keybase.io/alanmnichol) on keybase.
- I have a public key whose fingerprint is E4A6 5E28 9A74 DAE1 B53B BC9C 034A 0F94 FB6F 4774
To claim this, I am signing this object:
| import epic.models.{NerSelector, ParserSelector} | |
| import epic.parser.ParserAnnotator | |
| import epic.preprocess | |
| import epic.preprocess.{TreebankTokenizer, MLSentenceSegmenter} | |
| import epic.sequences.{SemiCRF, Segmenter} | |
| import epic.slab.{EntityMention, Token, Sentence} | |
| import epic.trees.{AnnotatedLabel, Tree} | |
| import epic.util.SafeLogging | |
I hereby claim:
To claim this, I am signing this object:
| %load dependenices | |
| run('~/Software/gpml/gpml-matlab-v3.4-2013-11-11/startup.m'); | |
| % params | |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| restart=true; | |
| filename='hypers22-Jan-2015.mat'; | |
| maxiters=20; | |
| npoints=3500; | |
| nsparse=2000; |
| from pymongo import MongoClient | |
| """ | |
| quick and dirty | |
| """ | |
| client=MongoClient() | |
| db=client['treevdb'] | |
| gdrivecoll=db['googledriveinformation'] | |
| cursor=gdrivecoll.find() |
| <Multipoles_Potentials> | |
| <Multipoles_params n_types="0" n_monomer_types="2" cutoff="10.0" method="direct" label="default" damping="erf" polarisation="none" intermolecular_only="T"> | |
| <monomer type='1' species='h2o' n_sites='3' signature="8_1_1" monomer_cutoff="2.0" > | |
| <per_site_data monomer="1" site='1' pos_type="atom" atomic_num="8" charge_method="FIXED" charge="-0.7" pol_alpha="0.0" damp_rad="1.0"/> | |
| <per_site_data monomer="1" site='2' pos_type="atom" atomic_num="1" charge_method="FIXED" charge="0.35" pol_alpha="0.0" damp_rad="1.0"/> | |
| Verifying that +alannichol is my blockchain ID. https://onename.com/alannichol |
| # I like using seaborn, but of course you can also just use this as a set of colours. | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import numpy as np | |
| # from seaborn docs | |
| def sinplot(flip=1): | |
| x = np.linspace(0, 14, 100) | |
| for i in range(1, 7): |
| while ( not formData.is_complete() ): | |
| questionKey = formData.first_missing_field() | |
| ask(questions[questionKey]) |
| class Embedding(object): | |
| def __init__(self,vocab_file,vectors_file): | |
| with open(vocab_file, 'r') as f: | |
| words = [x.rstrip().split(' ')[0] for x in f.readlines()] | |
| with open(vectors_file, 'r') as f: | |
| vectors = {} | |
| for line in f: | |
| vals = line.rstrip().split(' ') | |
| vectors[vals[0]] = [float(x) for x in vals[1:]] |
| def find_similar_words(embed,text,refs,thresh): | |
| C = np.zeros((len(refs),embed.W.shape[1])) | |
| for idx, term in enumerate(refs): | |
| if term in embed.vocab: | |
| C[idx,:] = embed.W[embed.vocab[term], :] | |
| tokens = text.split(' ') |