In [1]:
%pylab inline
Populating the interactive namespace from numpy and matplotlib
In [2]:
%load_ext Cython
In [3]:
import matplotlib.pyplot as plt
In [4]:
from nltk.corpus import stopwords
import xml.etree.ElementTree as ET
from tethne.readers import dfr
from tethne.classes.feature import StructuredFeature, StructuredFeatureSet
import os
import nltk
In [5]:
import cPickle as pickle
from collections import defaultdict, Counter
In [6]:
basepath = "/Users/erickpeirson/Downloads/JHB from JSTOR"
In [8]:
corpus = dfr.read(basepath)
In [69]:
corpus.indexed_papers
Out[69]:
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In [9]:
def chunkify(pages):
T = [] # Tokens.
PP = [] # Page start indices
P = [] # Paragraph start indices (in T).
S = [] # Sentence start indices (in T).
I = [] # Token start,end character position indices.
def _chunk_paragraphs(page):
try:
tok = nltk.tokenize.TextTilingTokenizer()
return tok.tokenize(page)
except ValueError: # No paragraphs
return [page]
def _chunk_sentences(para):
return nltk.tokenize.sent_tokenize(para)
pos = 0
for page in pages:
PP.append(len(T))
paragraphs = _chunk_paragraphs(page)
for para in paragraphs:
P.append(len(T)) # Paragraph start index in T.
sentences = _chunk_sentences(para)
for sent in sentences:
sent = sent.strip()
S.append(len(T)) # Sentence start index in T.
words = nltk.tokenize.word_tokenize(sent)
for word in words:
T.append(word)
word_length = len(word) + 1
I.append((pos, pos + word_length))
pos += word_length
return T, PP, P, S, I
In [78]:
ocrpath = os.path.join(basepath, 'ocr')
features = {}
u = 0
for fname in os.listdir(ocrpath):
print '\r', fname, u,
u += 1
if fname.lower().endswith('.xml'):
fpath = os.path.join(ocrpath, fname)
doi = fname[4:-4].replace('_', '/')
root = ET.parse(fpath).getroot()
T, PP, P, S, I = chunkify([page.text for page in root.findall('.//page')])
contexts = [
('page', PP),
('paragraph', P),
('sentence', S),
]
feature = StructuredFeature(T, contexts=contexts)
newTokens = []
newContexts = {}
tokenMap = {}
for i in xrange(len(feature)):
t = feature[i]
if keep(t):
tokenMap[len(newTokens)] = i
newTokens.append(transform(t))
reverse = {v:k for k,v in tokenMap.iteritems()}
newContexts = []
for name, indices in feature.contexts.items():
newIndices = []
for i in indices:
while i not in reverse:
if i > len(feature):
break
i += 1
if i not in reverse:
continue
new_i = reverse[i]
if new_i not in newIndices:
newIndices.append(new_i)
newContexts.append((name, newIndices))
newFeature = StructuredFeature(newTokens, newContexts, reference=(feature, tokenMap))
features[doi] = (newFeature)
ocr_10.2307_4332035.XML 1916
In [79]:
fset = StructuredFeatureSet(features)
In [24]:
def number(value):
for model in [int, float]:
try:
model(value)
return True
except:
pass
return False
In [20]:
stoplist = set(stopwords.words())
In [25]:
def keep(token):
reject = [len(token) < 3,
token in stoplist,
number(token)]
if not any(reject):
return True
return False
In [29]:
vocabulary = {}
def transform(token):
token = token.lower()
if token not in vocabulary:
vocabulary[token] = len(vocabulary)
return vocabulary[token]
In [11]:
papers, chunks = fset.context_chunks('page')
In [99]:
fset.N_features
Out[99]:
258084
In [9]:
%%cython -a
import numpy as np
cimport numpy as np
import cython
cimport cython
from cython cimport view
from libc.stdlib cimport malloc, free
DTYPE = np.float64
IDTYPE = np.int
ctypedef np.float64_t DTYPE_t
ctypedef np.int_t IDTYPE_t
cdef extern from "stdlib.h":
ctypedef void const_void "const void"
void qsort(void *base, int nmemb, int size,
int(*compar)(const_void *, const_void *)) nogil
cdef struct Sorter:
IDTYPE_t index
DTYPE_t value
cdef int _compare(const_void *a, const_void *b):
cdef DTYPE_t v = ((<Sorter*>a)).value-((<Sorter*>b)).value
if v < 0: return -1
if v >= 0: return 1
cdef void cyargsort(np.ndarray[DTYPE_t] data, Sorter * order):
cdef IDTYPE_t i
cdef IDTYPE_t n = data.shape[0]
for i in range(n):
order[i].index = i
order[i].value = data[i]
qsort(<void *> order, n, sizeof(Sorter), _compare)
cpdef argsort(np.ndarray[DTYPE_t] data, np.ndarray[IDTYPE_t] order):
cdef IDTYPE_t i
cdef IDTYPE_t n = data.shape[0]
cdef Sorter *order_struct = <Sorter *> malloc(n * sizeof(Sorter))
cyargsort(data, order_struct)
for i in range(n):
order[i] = order_struct[i].index
free(order_struct)
from libc.stdlib cimport rand
cdef extern from "stdlib.h":
int RAND_MAX
@cython.boundscheck(False)
@cython.cdivision(True)
def one_iter(np.ndarray[IDTYPE_t, ndim=2] n_dk,
np.ndarray[IDTYPE_t, ndim=2] n_kw,
np.ndarray[IDTYPE_t] n_k,
np.ndarray[IDTYPE_t] n_d,
np.ndarray[IDTYPE_t] z,
np.ndarray[DTYPE_t] alpha,
np.ndarray[DTYPE_t] beta,
np.ndarray[IDTYPE_t] words,
np.ndarray[IDTYPE_t] docs,
np.ndarray[DTYPE_t] p_z,
DTYPE_t B,
IDTYPE_t maxiters):
cdef unsigned int wmax = words.shape[0]
cdef unsigned int kmax = n_k.shape[0]
cdef DTYPE_t left
cdef DTYPE_t right
cdef IDTYPE_t n_kw_v, n_dk_v, n_d_v, n_k_v, k
cdef DTYPE_t beta_v, alpha_v, total
cdef DTYPE_t spz
cdef unsigned int w, i, d, word, it
cdef DTYPE_t rv
cdef np.ndarray[IDTYPE_t] p_z_sort = np.arange(kmax)
for it in range(maxiters):
for w in range(wmax):
d = docs[w]
word = words[w]
k = z[w]
# Decrement to exclude current topic assignment.
n_dk[d, k] -= 1
n_kw[word, k] -= 1
n_k[k] -= 1
# Recalculate probability of assigning z[i] = k
for i in range(kmax):
n_kw_v = n_kw[word, i]
n_dk_v = n_dk[d, i]
n_k_v = n_k[k]
alpha_v = alpha[i]
n_d_v = n_d[d]
beta_v = beta[word]
left = (n_dk_v + alpha_v) / (n_d_v + alpha_v)
right = (n_kw_v + beta_v)/(n_k_v + B)
p_z[i] = left * right
# Normalize to [0, 1]
spz = 0.
for i in range(kmax):
spz = spz + p_z[i]
for i in range(kmax):
p_z[i] = p_z[i] / spz
# Draw z[i] <- k from multinomial.
argsort(p_z, p_z_sort) # Sort parameters
rv = rand() / float(RAND_MAX) # rv ~ Uniform(0,1)
total = 0
for i in range(kmax):
total += p_z[p_z_sort[i]]
if rv < total:
k = p_z_sort[i]
break
z[w] = k
# Re-increment with new topic assignment.
n_dk[d, k] += 1
n_kw[word, k] +=1
n_k[k] += 1
return n_dk, n_kw, n_k, n_d, z
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003: cimport numpy as np
004: import cython
005: cimport cython
006: from cython cimport view
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036: cdef IDTYPE_t i
+037: cdef IDTYPE_t n = data.shape[0]
__pyx_v_n = (__pyx_v_data->dimensions[0]);
+038: cdef Sorter *order_struct = <Sorter *> malloc(n * sizeof(Sorter))
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043:
044: from libc.stdlib cimport rand
045: cdef extern from "stdlib.h":
046: int RAND_MAX
047:
048: @cython.boundscheck(False)
049: @cython.cdivision(True)
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__pyx_v_d = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_docs.rcbuffer->pybuffer.buf, __pyx_t_11, __pyx_pybuffernd_docs.diminfo[0].strides));
+076: word = words[w]
__pyx_t_12 = __pyx_v_w;
__pyx_v_word = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_words.rcbuffer->pybuffer.buf, __pyx_t_12, __pyx_pybuffernd_words.diminfo[0].strides));
077:
+078: k = z[w]
__pyx_t_13 = __pyx_v_w;
__pyx_v_k = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_z.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_z.diminfo[0].strides));
079:
+080: n_dk[d, k] -= 1
__pyx_t_14 = __pyx_v_d;
__pyx_t_15 = __pyx_v_k;
if (__pyx_t_15 < 0) __pyx_t_15 += __pyx_pybuffernd_n_dk.diminfo[1].shape;
*__Pyx_BufPtrStrided2d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_dk.rcbuffer->pybuffer.buf, __pyx_t_14, __pyx_pybuffernd_n_dk.diminfo[0].strides, __pyx_t_15, __pyx_pybuffernd_n_dk.diminfo[1].strides) -= 1;
+081: n_kw[word, k] -= 1
__pyx_t_16 = __pyx_v_word;
__pyx_t_17 = __pyx_v_k;
if (__pyx_t_17 < 0) __pyx_t_17 += __pyx_pybuffernd_n_kw.diminfo[1].shape;
*__Pyx_BufPtrStrided2d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_kw.rcbuffer->pybuffer.buf, __pyx_t_16, __pyx_pybuffernd_n_kw.diminfo[0].strides, __pyx_t_17, __pyx_pybuffernd_n_kw.diminfo[1].strides) -= 1;
+082: n_k[k] -= 1
__pyx_t_18 = __pyx_v_k;
if (__pyx_t_18 < 0) __pyx_t_18 += __pyx_pybuffernd_n_k.diminfo[0].shape;
*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_k.rcbuffer->pybuffer.buf, __pyx_t_18, __pyx_pybuffernd_n_k.diminfo[0].strides) -= 1;
083:
+084: for i in range(kmax):
__pyx_t_19 = __pyx_v_kmax;
for (__pyx_t_20 = 0; __pyx_t_20 < __pyx_t_19; __pyx_t_20+=1) {
__pyx_v_i = __pyx_t_20;
+085: n_kw_v = n_kw[word, i]
__pyx_t_21 = __pyx_v_word;
__pyx_t_22 = __pyx_v_i;
__pyx_v_n_kw_v = (*__Pyx_BufPtrStrided2d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_kw.rcbuffer->pybuffer.buf, __pyx_t_21, __pyx_pybuffernd_n_kw.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_n_kw.diminfo[1].strides));
+086: n_dk_v = n_dk[d, i]
__pyx_t_23 = __pyx_v_d;
__pyx_t_24 = __pyx_v_i;
__pyx_v_n_dk_v = (*__Pyx_BufPtrStrided2d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_dk.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_n_dk.diminfo[0].strides, __pyx_t_24, __pyx_pybuffernd_n_dk.diminfo[1].strides));
+087: n_k_v = n_k[k]
__pyx_t_25 = __pyx_v_k;
if (__pyx_t_25 < 0) __pyx_t_25 += __pyx_pybuffernd_n_k.diminfo[0].shape;
__pyx_v_n_k_v = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_k.rcbuffer->pybuffer.buf, __pyx_t_25, __pyx_pybuffernd_n_k.diminfo[0].strides));
+088: alpha_v = alpha[i]
__pyx_t_26 = __pyx_v_i;
__pyx_v_alpha_v = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_DTYPE_t *, __pyx_pybuffernd_alpha.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_alpha.diminfo[0].strides));
+089: n_d_v = n_d[d]
__pyx_t_27 = __pyx_v_d;
__pyx_v_n_d_v = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_d.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_n_d.diminfo[0].strides));
+090: beta_v = beta[word]
__pyx_t_28 = __pyx_v_word;
__pyx_v_beta_v = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_DTYPE_t *, __pyx_pybuffernd_beta.rcbuffer->pybuffer.buf, __pyx_t_28, __pyx_pybuffernd_beta.diminfo[0].strides));
091:
+092: left = (n_dk_v + alpha_v) / (n_d_v + alpha_v)
__pyx_v_left = ((__pyx_v_n_dk_v + __pyx_v_alpha_v) / (__pyx_v_n_d_v + __pyx_v_alpha_v));
+093: right = (n_kw_v + beta_v)/(n_k_v + B)
__pyx_v_right = ((__pyx_v_n_kw_v + __pyx_v_beta_v) / (__pyx_v_n_k_v + __pyx_v_B));
+094: p_z[i] = left * right
__pyx_t_29 = __pyx_v_i;
*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_DTYPE_t *, __pyx_pybuffernd_p_z.rcbuffer->pybuffer.buf, __pyx_t_29, __pyx_pybuffernd_p_z.diminfo[0].strides) = (__pyx_v_left * __pyx_v_right);
}
095:
+096: spz = 0.
__pyx_v_spz = 0.;
+097: for i in range(kmax):
__pyx_t_19 = __pyx_v_kmax;
for (__pyx_t_20 = 0; __pyx_t_20 < __pyx_t_19; __pyx_t_20+=1) {
__pyx_v_i = __pyx_t_20;
+098: spz = spz + p_z[i]
__pyx_t_30 = __pyx_v_i;
__pyx_v_spz = (__pyx_v_spz + (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_DTYPE_t *, __pyx_pybuffernd_p_z.rcbuffer->pybuffer.buf, __pyx_t_30, __pyx_pybuffernd_p_z.diminfo[0].strides)));
}
099:
+100: for i in range(kmax):
__pyx_t_19 = __pyx_v_kmax;
for (__pyx_t_20 = 0; __pyx_t_20 < __pyx_t_19; __pyx_t_20+=1) {
__pyx_v_i = __pyx_t_20;
+101: p_z[i] = p_z[i] / spz
__pyx_t_31 = __pyx_v_i;
__pyx_t_32 = __pyx_v_i;
*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_DTYPE_t *, __pyx_pybuffernd_p_z.rcbuffer->pybuffer.buf, __pyx_t_32, __pyx_pybuffernd_p_z.diminfo[0].strides) = ((*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_DTYPE_t *, __pyx_pybuffernd_p_z.rcbuffer->pybuffer.buf, __pyx_t_31, __pyx_pybuffernd_p_z.diminfo[0].strides)) / __pyx_v_spz);
}
102:
+103: argsort(p_z, p_z_sort) # Sort parameters
__pyx_t_1 = __pyx_f_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_argsort(((PyArrayObject *)__pyx_v_p_z), ((PyArrayObject *)__pyx_v_p_z_sort), 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 103; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+104: rv = rand() / float(RAND_MAX) # rv ~ Uniform(0,1)
__pyx_v_rv = (rand() / ((double)RAND_MAX));
105:
+106: total = 0
__pyx_v_total = 0.0;
+107: for i in range(kmax):
__pyx_t_19 = __pyx_v_kmax;
for (__pyx_t_20 = 0; __pyx_t_20 < __pyx_t_19; __pyx_t_20+=1) {
__pyx_v_i = __pyx_t_20;
+108: total += p_z[p_z_sort[i]]
__pyx_t_33 = __pyx_v_i;
__pyx_t_34 = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_p_z_sort.rcbuffer->pybuffer.buf, __pyx_t_33, __pyx_pybuffernd_p_z_sort.diminfo[0].strides));
if (__pyx_t_34 < 0) __pyx_t_34 += __pyx_pybuffernd_p_z.diminfo[0].shape;
__pyx_v_total = (__pyx_v_total + (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_DTYPE_t *, __pyx_pybuffernd_p_z.rcbuffer->pybuffer.buf, __pyx_t_34, __pyx_pybuffernd_p_z.diminfo[0].strides)));
+109: if rv < total:
__pyx_t_35 = ((__pyx_v_rv < __pyx_v_total) != 0);
if (__pyx_t_35) {
+110: k = p_z_sort[i]
__pyx_t_36 = __pyx_v_i;
__pyx_v_k = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_p_z_sort.rcbuffer->pybuffer.buf, __pyx_t_36, __pyx_pybuffernd_p_z_sort.diminfo[0].strides));
+111: break
goto __pyx_L14_break;
}
}
__pyx_L14_break:;
112: # k = sample(p_z, rv)
113:
+114: z[w] = k
__pyx_t_19 = __pyx_v_w;
*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_z.rcbuffer->pybuffer.buf, __pyx_t_19, __pyx_pybuffernd_z.diminfo[0].strides) = __pyx_v_k;
+115: n_dk[d, k] += 1
__pyx_t_20 = __pyx_v_d;
__pyx_t_37 = __pyx_v_k;
if (__pyx_t_37 < 0) __pyx_t_37 += __pyx_pybuffernd_n_dk.diminfo[1].shape;
*__Pyx_BufPtrStrided2d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_dk.rcbuffer->pybuffer.buf, __pyx_t_20, __pyx_pybuffernd_n_dk.diminfo[0].strides, __pyx_t_37, __pyx_pybuffernd_n_dk.diminfo[1].strides) += 1;
+116: n_kw[word, k] +=1
__pyx_t_38 = __pyx_v_word;
__pyx_t_39 = __pyx_v_k;
if (__pyx_t_39 < 0) __pyx_t_39 += __pyx_pybuffernd_n_kw.diminfo[1].shape;
*__Pyx_BufPtrStrided2d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_kw.rcbuffer->pybuffer.buf, __pyx_t_38, __pyx_pybuffernd_n_kw.diminfo[0].strides, __pyx_t_39, __pyx_pybuffernd_n_kw.diminfo[1].strides) += 1;
+117: n_k[k] += 1
__pyx_t_40 = __pyx_v_k;
if (__pyx_t_40 < 0) __pyx_t_40 += __pyx_pybuffernd_n_k.diminfo[0].shape;
*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_d9c528fd3d73ccd6a85d7b47c951dc1c_IDTYPE_t *, __pyx_pybuffernd_n_k.rcbuffer->pybuffer.buf, __pyx_t_40, __pyx_pybuffernd_n_k.diminfo[0].strides) += 1;
}
}
118:
+119: return n_dk, n_kw, n_k, n_d, z
__Pyx_XDECREF(__pyx_r);
__pyx_t_1 = PyTuple_New(5); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 119; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_INCREF(((PyObject *)__pyx_v_n_dk));
PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)__pyx_v_n_dk));
__Pyx_GIVEREF(((PyObject *)__pyx_v_n_dk));
__Pyx_INCREF(((PyObject *)__pyx_v_n_kw));
PyTuple_SET_ITEM(__pyx_t_1, 1, ((PyObject *)__pyx_v_n_kw));
__Pyx_GIVEREF(((PyObject *)__pyx_v_n_kw));
__Pyx_INCREF(((PyObject *)__pyx_v_n_k));
PyTuple_SET_ITEM(__pyx_t_1, 2, ((PyObject *)__pyx_v_n_k));
__Pyx_GIVEREF(((PyObject *)__pyx_v_n_k));
__Pyx_INCREF(((PyObject *)__pyx_v_n_d));
PyTuple_SET_ITEM(__pyx_t_1, 3, ((PyObject *)__pyx_v_n_d));
__Pyx_GIVEREF(((PyObject *)__pyx_v_n_d));
__Pyx_INCREF(((PyObject *)__pyx_v_z));
PyTuple_SET_ITEM(__pyx_t_1, 4, ((PyObject *)__pyx_v_z));
__Pyx_GIVEREF(((PyObject *)__pyx_v_z));
__pyx_r = __pyx_t_1;
__pyx_t_1 = 0;
goto __pyx_L0;
In [14]:
D = len(chunks)
W = fset.N_features
words = np.array([ c for chunk in chunks for c in chunk])
docs = np.array([ d for d in xrange(D) for c in chunks[d] ])
N = len(words)
K = 200
n_dk = np.zeros((D, K), dtype=np.int) # Number of words in d assigned to k
n_kw = np.zeros((W, K), dtype=np.int) # Number of times word w is assigned to k
n_k = np.zeros((K, ), dtype=np.int)
n_d = np.zeros((D, ), dtype=np.int)
z = randint.rvs(0, K, size=N)
for w in xrange(N):
d = docs[w]
word = words[w]
k = z[w]
n_dk[d, k] += 1
n_kw[word, k] += 1
n_k[k] += 1
n_d[d] += 1
w += 1
alpha = uniform.rvs(0., 50./K, size=K)
# beta = uniform.rvs(0., 200./W, size=W)
beta = np.array([200./W]*W)
In [15]:
n_dk, n_kw, n_k, n_d, z = one_iter(n_dk, n_kw, n_k, n_d, z, alpha, beta, words, docs, np.zeros((K,)), np.sum(beta), 10)
In [23]:
z[33000:33300]
Out[23]:
array([ 95, 119, 114, 31, 100, 100, 100, 100, 100, 95, 100, 100, 100,
100, 100, 109, 100, 100, 100, 162, 100, 109, 109, 100, 100, 100,
100, 100, 186, 100, 100, 100, 109, 100, 100, 100, 100, 100, 100,
95, 121, 100, 100, 109, 165, 100, 64, 100, 100, 100, 43, 162,
100, 100, 100, 100, 114, 100, 198, 100, 186, 100, 162, 162, 64,
100, 100, 100, 121, 100, 100, 100, 64, 100, 109, 100, 100, 109,
100, 165, 109, 100, 40, 162, 19, 165, 121, 100, 100, 95, 100,
119, 100, 109, 100, 100, 100, 100, 100, 121, 162, 100, 95, 95,
100, 100, 100, 100, 65, 100, 100, 100, 100, 109, 109, 109, 100,
100, 119, 100, 109, 109, 100, 139, 100, 100, 114, 100, 109, 100,
162, 119, 100, 100, 109, 162, 100, 37, 100, 100, 100, 100, 109,
121, 100, 100, 100, 162, 100, 100, 100, 165, 109, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 175, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 109, 100, 109, 100, 162, 59, 100, 100,
100, 100, 100, 100, 100, 100, 37, 101, 100, 100, 109, 100, 182,
182, 95, 47, 100, 100, 175, 162, 100, 95, 121, 100, 100, 100,
100, 100, 100, 95, 100, 95, 100, 186, 162, 100, 100, 64, 100,
100, 100, 121, 170, 100, 100, 109, 100, 109, 100, 100, 100, 100,
109, 109, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 36, 95, 100, 100, 100, 100, 100, 100, 100, 109,
100, 100, 100, 100, 100, 109, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 95, 109, 100, 100, 100, 100, 100, 100, 100, 121,
100, 109, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 109, 109])
In [83]:
with open('/Users/erickpeirson/Desktop/jhb_featureset.pickle', 'w') as f:
pickle.dump(fset, f)
In [7]:
with open('/Users/erickpeirson/Desktop/jhb_featureset.pickle', 'r') as f:
fset = pickle.load(f)
In [20]:
import json
with open('/Users/erickpeirson/Projects/jhb_fixture.json', 'r') as f:
data = json.load(f)
In [13]:
from scipy.stats import uniform, rv_discrete, randint
import numpy as np
Content source: erickpeirson/statistical-computing
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