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(' ') |