def test_insert(self):
forest = MinHashLSHForest()
mg = WeightedMinHashGenerator(10)
m1 = mg.minhash(np.random.uniform(1, 10, 10))
m2 = mg.minhash(np.random.uniform(1, 10, 10))
forest.add("a", m1)
forest.add("b", m2)
self.assertTrue(forest.is_empty())
for t in forest.hashtables:
self.assertTrue(len(t) >= 1)
items = []
for H in t:
items.extend(t[H])
self.assertTrue("a" in items)
self.assertTrue("b" in items)
self.assertTrue("a" in forest)
self.assertTrue("b" in forest)
for i, H in enumerate(forest.keys["a"]):
self.assertTrue("a" in forest.hashtables[i][H])
forest.index()
self.assertFalse(forest.is_empty())
mg = WeightedMinHashGenerator(10, 5)
m3 = mg.minhash(np.random.uniform(1, 10, 10))
self.assertRaises(ValueError, forest.add, "c", m3)
def run_perf(dim, num_rep, sample_size):
wmg = WeightedMinHashGenerator(dim, sample_size=sample_size)
logging.info("WeightedMinHash using %d samples" % sample_size)
data = np.random.uniform(0, dim, (num_rep, dim))
durs = []
for i in range(num_rep):
start = time.clock()
wmg.minhash(data[i])
duration = (time.clock() - start) * 1000
durs.append(duration)
ave = np.mean(durs)
logging.info("Generated %d minhashes, average time %.4f ms" % (num_rep, ave))
return ave
def run_perf(dim, num_rep, sample_size):
wmg = WeightedMinHashGenerator(dim, sample_size=sample_size)
logging.info("WeightedMinHash using %d samples" % sample_size)
data = np.random.uniform(0, dim, (num_rep, dim))
durs = []
for i in range(num_rep):
start = time.clock()
wmg.minhash(data[i])
duration = (time.clock() - start) * 1000
durs.append(duration)
ave = np.mean(durs)
logging.info("Generated %d minhashes, average time %.4f ms" %
(num_rep, ave))
return ave
def test_query(self):
forest = MinHashLSHForest()
mg = WeightedMinHashGenerator(10)
m1 = mg.minhash(np.random.uniform(1, 10, 10))
m2 = mg.minhash(np.random.uniform(1, 10, 10))
forest.add("a", m1)
forest.add("b", m2)
forest.index()
result = forest.query(m1, 2)
self.assertTrue("a" in result)
self.assertTrue("b" in result)
mg = WeightedMinHashGenerator(10, 5)
m3 = mg.minhash(np.random.uniform(1, 10, 10))
self.assertRaises(ValueError, forest.query, m3, 1)
def test_calc_tiny(self):
v1 = [
1, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 6, 7, 8, 0, 0, 0, 0, 0, 0, 9, 10,
4
]
v2 = [
2, 0, 0, 0, 4, 3, 8, 0, 0, 0, 0, 4, 7, 10, 0, 0, 0, 0, 0, 0, 9, 0,
0
]
bgen = WeightedMinHashGenerator(len(v1))
gen = libMHCUDA.minhash_cuda_init(len(v1), 128, devices=1, verbosity=2)
libMHCUDA.minhash_cuda_assign_vars(gen, bgen.rs, bgen.ln_cs,
bgen.betas)
m = csr_matrix(numpy.array([v1, v2], dtype=numpy.float32))
hashes = libMHCUDA.minhash_cuda_calc(gen, m)
libMHCUDA.minhash_cuda_fini(gen)
self.assertEqual(hashes.shape, (2, 128, 2))
true_hashes = numpy.array(
[bgen.minhash(v1).hashvalues,
bgen.minhash(v2).hashvalues],
dtype=numpy.uint32)
self.assertEqual(true_hashes.shape, (2, 128, 2))
try:
self.assertTrue((hashes == true_hashes).all())
except AssertionError as e:
print("---- TRUE ----")
print(true_hashes)
print("---- FALSE ----")
print(hashes)
raise e from None
def weighed_min_hash(self,
num_perm=64,
seed=42,
use_components=None,
type_option=None,
n_char=None,
n_word=None,
npz=None,
isrequest=False):
""" """
if npz:
self.options = type_option
self.num_perm = num_perm
n = n_char if type_option == 'char' else n_word
self.features['weighed_{}_{}_{}minhash'.format(use_components[0], type_option[0], n[0])] = \
np.load(npz)['min_hash']
return self
use_components = use_components or ['name']
type_option = type_option or ['char']
n_char = n_char or [3]
n_word = n_word or [1]
if 'char' not in type_option and 'word' not in type_option:
assert False, "Проверьте значение параметра type_option."
if 'name' not in use_components and 'addr' not in use_components:
assert False, "Проверьте значение параметра use_components."
self.options = type_option
self.num_perm = num_perm
for i in use_components:
for j in type_option:
n_list = n_char if j == 'char' else n_word
for n in n_list:
wmg = WeightedMinHashGenerator(len(
self.features['tf_idf_{}_{}_{}grams'.format(i, j,
n)][0]),
sample_size=num_perm,
seed=seed)
help_list = []
for vector in self.features['tf_idf_{}_{}_{}grams'.format(
i, j, n)]:
if np.all(vector == 0):
vector[0] = 0.000001 # Это костылек.
help_list.append(wmg.minhash(vector))
self.features['weighed_{}_{}_{}minhash'.format(
i, j, n)] = np.array(help_list)
file_path = 'data/min_hash_dadata/{}_{}_{}_weighed_minhash.npz'.format(
i, j, n)
if not isrequest:
np.savez_compressed(file_path,
min_hash=np.array(help_list))
del self.features['tf_idf_{}_{}_{}grams'.format(i, j, n)]
return self
def GetFingerPoint(self, hashbit):
import simhash
import mynilsimsa
from datasketch import WeightedMinHashGenerator
"""
schar=[]
for iy in range(len(self.wlData)):
tsc=[]
for ix in range(len(self.wlData[0])):
if(self.wlData[iy,ix]==1):
tsc.append('a')
elif(self.wlData[iy,ix]==-1):
tsc.append('c')
else:
tsc.append('d')
schar.append(tsc)
for cr in schar:
hh=simhash.simhash(''.join(cr),hashbits=hashbit)
self.hash.append(hh.hash)
"""
#"""
wmg = WeightedMinHashGenerator(len(self.wlData[0]),
sample_size=2,
seed=12)
for tr in self.wlData:
try:
wm = wmg.minhash(tr) # wm1 is of the type WeightedMinHash
vl = np.transpose(wm.hashvalues)
vl = vl[0]
self.hash.append(vl.tolist())
except:
print(tr)
def _test_calc_big(self, devices):
numpy.random.seed(0)
data = numpy.random.randint(0, 100, (6400, 130))
mask = numpy.random.randint(0, 5, data.shape)
data *= (mask >= 4)
del mask
bgen = WeightedMinHashGenerator(data.shape[-1])
gen = libMHCUDA.minhash_cuda_init(data.shape[-1], 128, devices=devices, verbosity=2)
libMHCUDA.minhash_cuda_assign_vars(gen, bgen.rs, bgen.ln_cs, bgen.betas)
m = csr_matrix(data, dtype=numpy.float32)
print(m.nnz / (m.shape[0] * m.shape[1]))
ts = time()
hashes = libMHCUDA.minhash_cuda_calc(gen, m)
print("libMHCUDA:", time() - ts)
libMHCUDA.minhash_cuda_fini(gen)
self.assertEqual(hashes.shape, (len(data), 128, 2))
ts = time()
true_hashes = numpy.array([bgen.minhash(line).hashvalues for line in data],
dtype=numpy.uint32)
print("datasketch:", time() - ts)
self.assertEqual(true_hashes.shape, (len(data), 128, 2))
try:
self.assertTrue((hashes == true_hashes).all())
except AssertionError as e:
for r in range(hashes.shape[0]):
if (hashes[r] != true_hashes[r]).any():
print("first invalid row:", r)
print(hashes[r])
print(true_hashes[r])
break
raise e from None
def main():
data1 = ['this', 'is', 'a', 'pen']
data2 = ['that', 'is', 'a', 'pen']
data3 = ['it', 'is', 'a', 'pon']
m1 = get_minhash(data1)
m2 = get_minhash(data2)
m3 = get_minhash(data3)
print('minhash:')
print(m1.jaccard(m2))
print(m1.jaccard(m3))
print(m2.jaccard(m3))
print()
v1 = [1, 2, 3, 4, 5, 6]
v2 = [2, 5, 7, 9, 11, 13]
v3 = [1, 2, 3, 4, 5, 7]
wmg = WeightedMinHashGenerator(len(v1))
wm1 = wmg.minhash(v1)
wm2 = wmg.minhash(v2)
wm3 = wmg.minhash(v3)
print('weighted minhash:')
print(wm1.jaccard(wm2))
print(wm1.jaccard(wm3))
print(wm2.jaccard(wm3))
print()
data = data1 + data2 + data3
hll = get_hyperloglog(data)
print('hyperloglog:')
print(hll.count())
print()
hpp = get_hyperloglog_pp(data)
print('hyperloglog++:')
print(hpp.count())
print()
print('done')
def test__H(self):
'''
Check _H output consistent bytes length given
the same concatenated hash value size
'''
mg = WeightedMinHashGenerator(100, sample_size=128)
for l in range(2, mg.sample_size+1, 16):
m = mg.minhash(np.random.randint(1, 99999999, 100))
forest = MinHashLSHForest(num_perm=128, l=l)
forest.add("m", m)
sizes = [len(H) for ht in forest.hashtables for H in ht]
self.assertTrue(all(sizes[0] == s for s in sizes))
def test_pickle(self):
forest = MinHashLSHForest()
mg = WeightedMinHashGenerator(10)
m1 = mg.minhash(np.random.uniform(1, 10, 10))
m2 = mg.minhash(np.random.uniform(1, 10, 10))
forest.add("a", m1)
forest.add("b", m2)
forest.index()
forest2 = pickle.loads(pickle.dumps(forest))
result = forest2.query(m1, 2)
self.assertTrue("a" in result)
self.assertTrue("b" in result)
def avoid_unkown_error(candidate_eids, word_vectors):
wmg = WeightedMinHashGenerator(word_vectors.shape[1], sample_size=300)
wm = list() # Calculating wm takes time!!!
__candidate_eids = []
for i, eid in tqdm(zip(range(len(candidate_eids)), candidate_eids),
total=len(candidate_eids)):
try:
wm.append(wmg.minhash(word_vectors[i]))
__candidate_eids.append(eid)
except ValueError as e:
pass
return set(__candidate_eids), wmg, wm
def tiny_test():
v1 = [1, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 6, 7, 8, 0, 0, 0, 0, 0, 0, 9, 10, 4]
v2 = [2, 0, 0, 0, 4, 3, 8, 0, 0, 0, 0, 4, 7, 10, 0, 0, 0, 0, 0, 0, 9, 0, 0]
bgen = WeightedMinHashGenerator(len(v1), 128, 1)
write_csv_float("tiny-rs.csv", bgen.rs)
write_csv_float("tiny-ln_cs.csv", bgen.ln_cs)
write_csv_float("tiny-betas.csv", bgen.betas)
write_csv_int("tiny-data.csv", [v1, v2])
write_csv_int("tiny-hashes-0.csv", bgen.minhash(v1).hashvalues)
write_csv_int("tiny-hashes-1.csv", bgen.minhash(v2).hashvalues)
def run_acc(dim, num_rep, sample_size):
logging.info("WeightedMinHash using %d samples" % sample_size)
wmg = WeightedMinHashGenerator(dim, sample_size=sample_size)
data1 = np.random.uniform(0, dim, (num_rep, dim))
data2 = np.random.uniform(0, dim, (num_rep, dim))
errs = []
for i in range(num_rep):
wm1 = wmg.minhash(data1[i])
wm2 = wmg.minhash(data2[i])
j_e = wm1.jaccard(wm2)
j = jaccard(data1[i], data2[i])
errs.append(abs(j - j_e))
ave = np.mean(errs)
logging.info("%d runs, mean error %.4f" % (num_rep, ave))
return ave
def run_acc(dim, num_rep, sample_size):
logging.info("WeightedMinHash using %d samples" % sample_size)
wmg = WeightedMinHashGenerator(dim, sample_size=sample_size)
data1 = np.random.uniform(0, dim, (num_rep, dim))
data2 = np.random.uniform(0, dim, (num_rep, dim))
errs = []
for i in range(num_rep):
wm1 = wmg.minhash(data1[i])
wm2 = wmg.minhash(data2[i])
j_e = wm1.jaccard(wm2)
j = jaccard(data1[i], data2[i])
errs.append(abs(j - j_e))
ave = np.mean(errs)
logging.info("%d runs, mean error %.4f" % (num_rep, ave))
return ave
def big_test():
numpy.random.seed(0)
data = numpy.random.randint(0, 100, (6400, 130))
mask = numpy.random.randint(0, 5, data.shape)
data *= (mask >= 4)
del mask
bgen = WeightedMinHashGenerator(data.shape[-1])
write_csv_float("big-rs.csv", bgen.rs)
write_csv_float("big-ln_cs.csv", bgen.ln_cs)
write_csv_float("big-betas.csv", bgen.betas)
write_csv_int("big-data.csv", data)
c = 0
for line in data:
write_csv_int("big-hashes-%d.csv" % c, bgen.minhash(line).hashvalues)
c = c + 1
def get_250nearestNeighbors(seedEids,
candidate_eids,
word_vectors,
idxByCandEidMap,
data,
print_info,
query_id,
choice,
wmg=None,
wm=None):
# choice == 1: return 250 nearest Neighbors based on Jaccard Similarity with seeds.
# choice == 2: return 250 nearest Neighbors based on word embeddings.
start = time.time()
if wmg is None or wm is None:
wmg = WeightedMinHashGenerator(word_vectors.shape[1],
sample_size=300)
wm = list() # Calculating wm takes time!!!
for i in range(len(candidate_eids)):
wm.append(wmg.minhash(word_vectors[i]))
distToSeedsByEid = get_distToSeedsByEid(candidate_eids, wm, seedEids,
idxByCandEidMap, data, choice)
nearestNeighbors = []
ct = 0
for eid in sorted(distToSeedsByEid,
key=distToSeedsByEid.__getitem__,
reverse=True):
if ct >= 250:
break
if eid not in seedEids:
nearestNeighbors.append(eid)
ct += 1
assert ct >= 250
# print 'Nearest Neighbors are: '
# for i in nearestNeighbors:
# print eidToEntityMap[i],
# print ' '
end = time.time()
print(
'[utils.py] Done finding 250 nearest neighbors using %.1f seconds'
% (end - start))
print_info[query_id] += (
'[utils.py] Done finding 250 nearest neighbors using %.1f seconds\n'
% (end - start))
return nearestNeighbors, wmg, wm
def create_minhash(doc):
"""
This function takes the weighted representation of a product and returns its Minhash signature.
:param doc: The weighted representation of the product
:return: The Minhash signature of the product as a Minhash object
"""
term_ids = doc['term_id']
tfidfs = doc['tfidf']
tfidf_list = [0]*35405
i = 0
for term_id in term_ids:
tfidf_list[term_id] = tfidfs[i]
i += 1
m1 = mg.minhash(tfidf_list)
return m1
# create a minhash for each row(product) by calling the 'create_minhash' function
df_tfidf['Minhash'] = df_tfidf[0:].apply(lambda x: create_minhash(x), axis=1)
# create a list with all the Minhash signatures
minhash_list = df_tfidf['Minhash']
# create a MinHashLSHForest object with num_perm parameter equal to sample_size(=128)
# num_perm: the number of permutation functions
forest = MinHashLSHForest(num_perm=128)
# add each Minhash signature into the index
i = 0
from datasketch import WeightedMinHashGenerator
import numpy as np
v1 = [1, 3, 4, 5, 6, 7, 8, 9, 10, 4]
v2 = [2, 4, 3, 8, 4, 7, 10, 9, 0, 0]
min_sum = np.sum(np.minimum(v1, v2))
max_sum = np.sum(np.maximum(v1, v2))
true_jaccard = float(min_sum) / float(max_sum)
wmg = WeightedMinHashGenerator(len(v1))
wm1 = wmg.minhash(v1)
wm2 = wmg.minhash(v2)
print("Estimated Jaccard is", wm1.jaccard(wm2))
print("True Jaccard is", true_jaccard)
class SacFig():
def GetData(self, file):
st = pread(file)
return st[0].data
def __init__(self, staName, force=False):
self.wlWinN = 300
self.wlLagN = 10
self.fqWinN = 300
self.fqLagN = 10
self.fqRspN = 32
self.wlRspN = 32
self.selmax = 30
self.wl_x_level = 3
self.vectLen = self.fqRspN * self.wlRspN
self.wmg = WeightedMinHashGenerator(self.vectLen,
sample_size=2,
seed=12)
self.sta = False
self.sphs = self.fqLagN * self.wlLagN / 100
self.GetSta(staName, force=force)
#tempdata=self.GetData("D:/Weiyuan/templates/2015318092941.s15.z")
def GetHash(self):
self.hash = []
step = self.fqLagN * self.wlLagN
for itr in range(self.datalen):
data = self.STFT(self.dt, itr * step)
data = self.WAVELET(data, self.wl_x_level)
data = self.REGU(data)
data = self.TRIM(data, self.selmax)
data = self.FIG(data)
self.hash.append(data)
return self.hash
def GetHashTofile(self, fileName, outfile, force=False):
ext = [
DIR + "s28/2015336/2015336_00_00_00_s28_BHZ.SAC",
DIR + "s28/2015336/2015336_00_00_00_s28_BHN.SAC",
DIR + "s28/2015336/2015336_00_00_00_s28_BHE.SAC"
]
dt = []
if (len(self.GetData(fileName[0])) < 10000):
#print(len(self.GetData(fileName[0])<10000))
for fn in range(len(fileName)):
ddt = self.GetData(ext[fn])[:10000]
cp = len(self.GetData(fileName[fn]))
ddt[:cp] = ddt[:cp] + np.zeros(
[cp]) #+self.GetData(fileName[fn])
dt.append(ddt)
else:
for fn in fileName:
dt.append(self.GetData(fn)[:500000])
datalen = int((len(dt[0]) - self.wlLagN * self.wlWinN - self.fqWinN) /
self.fqLagN / self.wlLagN)
file = open(outfile, "w")
step = self.fqLagN * self.wlLagN
for itr in range(datalen):
data = self.STFT(dt, itr * step)
data = self.WAVELET(data, 3)
data = self.REGU(data)
data = self.TRIM(data, self.selmax)
try:
data = self.FIG(data)
except:
data = [0, 0]
tm = itr * self.sphs
file.write("%f," % (tm))
for itr in data:
file.write("%d," % itr)
file.write("\n")
file.close()
def GetSta(self, fileName, force=False):
if (force == True):
self.sta = False
if (os.path.exists("stat.out.npz") == True):
os.remove("stat.out.npz")
if (self.sta == True):
return
if (os.path.exists("stat.out.npz") == True):
data = np.load("stat.out.npz")
self.mu = data['a']
self.sigma = data['b']
self.sta = True
return
dt_for_sta = np.zeros([4000, self.vectLen])
dt = []
for fn in fileName:
dt.append(self.GetData(fn))
for idx in range(4000):
data = self.STFT(dt, idx * 1000)
dt_for_sta[idx, :] = self.WAVELET(data, 3)
self.mu = np.average(dt_for_sta, axis=0)
self.sigma = np.std(dt_for_sta, axis=0)
np.savez("stat.out.npz", a=self.mu, b=self.sigma)
self.sta = True
def STFT(self, xx, idx):
fqWin = int(self.fqWinN / 2)
sumx = np.zeros([self.wlWinN, fqWin])
tpx = np.zeros([self.wlWinN, self.fqWinN])
for itx in xx:
w = np.hanning(self.fqWinN)
tpx[:, :] = np.zeros([self.wlWinN, self.fqWinN])
for ii in range(self.wlWinN):
start = ii * self.fqLagN + idx
tpx[ii, :] = itx[start:start + self.fqWinN]
X = scipy.fft(tpx, axis=1)
X = (X * w)[:, :fqWin]
X = np.square(np.abs(X))
sumx = np.add(sumx, X)
sumx = np.sqrt(sumx)
sumx = resample(sumx, self.wlRspN, axis=0)
sumx = resample(sumx, self.fqRspN, axis=1)
return sumx
def WAVELET(self, data, level):
outdt = pywt.wavedec2(data, 'haar', level=level)
out = np.zeros([self.vectLen])
idx = 0
for itr in outdt:
evaldt = np.reshape(itr, [-1])
cit = len(evaldt)
out[idx:idx + cit] = evaldt[:]
idx += cit
out2 = np.sqrt(np.sum(np.square(out)))
out = np.divide(out, out2)
return out
def REGU(self, data):
rdata = data - self.mu
rdata = np.divide(rdata, self.sigma)
return rdata
def TRIM(self, data, nlarge):
absdata = np.abs(data)
large = heapq.nlargest(nlarge,
range(len(data)),
key=lambda x: absdata[x])
lst = np.zeros_like(data)
for itr in large:
lst[itr] = data[itr] / absdata[itr]
return lst
def FIG(self, tr):
wm = self.wmg.minhash(tr) # wm1 is of the type WeightedMinHash
vl = np.transpose(wm.hashvalues)
vl = vl[0]
return (vl.tolist())
def create_weighted_minhash(data):
global wmg
if not wmg:
wmg = WeightedMinHashGenerator(len(data), 128, seed=12)
minhash = wmg.minhash(data)
return minhash
def calcuJaccard(hashSig1, hashSig2):
return np.float(np.count_nonzero(hashSig1 == hashSig2)) /\
np.float(len(hashSig1))
if __name__ == '__main__':
data1 = [
'TAG', 'DSA', 'FDV', 'GFG', 'TRE', 'EWE', 'QWW', 'RFR', 'QWE', 'ZAW',
'CDS', 'NBH'
]
data2 = [
'TAG', 'DSA', 'FDV', 'GFG', 'TRE', 'EWE', 'QWW', 'RFR', 'SDA', 'ZAW',
'CDS', 'NBH'
]
m1, m2 = MinHash(), MinHash()
for d in data1:
m1.update(d.encode('utf8'))
for d in data2:
m2.update(d.encode('utf8'))
print("Estimated Jaccard for data1 and data2 is", m1.jaccard(m2))
data3 = [2, 0, 0, 0, 0, 3, 0, 0, 2, 0, 2, 0, 0]
data4 = [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0]
weight = WeightedMinHashGenerator(len(data3))
w1 = weight.minhash(data3)
w2 = weight.minhash(data4)
print("Weighted Jac ", w1.jaccard(w2))
class SacFig():
def GetData(self, file):
st = pread(file)
return st[0].data
def __init__(self, fileName):
self.wlWinN = 200
self.wlLagN = 10
self.fqWinN = 200
self.fqLagN = 10
self.fqRspN = 32
self.wlRspN = 32
self.selmax = 50
self.wl_x_level = 3
self.vectLen = self.fqRspN * self.wlRspN
self.wmg = WeightedMinHashGenerator(self.vectLen,
sample_size=2,
seed=12)
self.sta = False
self.sphs = self.fqLagN * self.wlLagN / 100
self.GetSta(fileName, force=True)
for fn in fileName:
self.dt = self.GetData(fileName)[:500000]
self.datalen = int(
(len(self.dt) - self.wlLagN * self.wlWinN - self.fqWinN) /
self.fqLagN / self.wlLagN)
#tempdata=self.GetData("D:/Weiyuan/templates/2015318092941.s15.z")
tpd = np.load("template.npz")
tempdata = tpd['c']
for itrx in range(6):
itr = np.random.randint(100)
start = itrx * 50000 + 127
end = start + len(tempdata[itr])
#print(start,end,itr,np.shape(tempdata[itr]),np.shape(self.dt[start:end]))
self.dt[start:end] = self.dt[start:end] + tempdata[itr] * 0.1
x = np.linspace(0, 5000, len(self.dt))
plt.plot(x, self.dt)
plt.show()
def GetHash(self):
self.hash = []
step = self.fqLagN * self.wlLagN
for itr in range(self.datalen):
data = self.STFT(self.dt, itr * step)
data = self.WAVELET(data, self.wl_x_level)
data = self.REGU(data)
data = self.TRIM(data, self.selmax)
data = self.FIG(data)
self.hash.append(data)
return self.hash
def GetHashTofile(self, file):
self.hash = []
step = self.fqLagN * self.wlLagN
for itr in range(self.datalen):
data = self.STFT(self.dt, itr * step)
data = self.WAVELET(data, 3)
data = self.REGU(data)
data = self.TRIM(data, self.selmax)
data = self.FIG(data)
self.hash.append(data)
tm = itr * self.sphs
file.write("%f," % (tm))
for itr in data:
file.write("%d," % itr)
file.write("\n")
def GetSta(self, fileName, force=False):
if (force == True):
self.sta = False
os.remove("stat.out.npz")
if (self.sta == True):
return
if (os.path.exists("stat.out.npz") == True):
data = np.load("stat.out.npz")
self.mu = data['a']
self.sigma = data['b']
self.sta = True
return
dt_for_sta = np.zeros([4000, self.vectLen])
dta = self.GetData(fileName)
for idx in range(4000):
dt = self.STFT(dta, idx * 1000)
dt_for_sta[idx, :] = self.WAVELET(dt, 3)
self.mu = np.average(dt_for_sta, axis=0)
self.sigma = np.std(dt_for_sta, axis=0)
np.savez("stat.out.npz", a=self.mu, b=self.sigma)
self.sta = True
def STFT(self, x, idx):
w = np.hanning(self.fqWinN)
fqWin = int(self.fqWinN / 2)
X = np.zeros([self.wlWinN, self.fqWinN])
for ii in range(self.wlWinN):
start = ii * self.fqLagN + idx
X[ii, :] = x[start:start + self.fqWinN] * w
X = scipy.fft(X, axis=1)[:fqWin]
X = np.abs(np.array(X))
X = resample(X, self.wlRspN, axis=0)
X = resample(X, self.fqRspN, axis=1)
return X
def WAVELET(self, data, level):
outdt = pywt.wavedec2(data, 'haar', level=level)
out = np.zeros([self.vectLen])
idx = 0
for itr in outdt:
evaldt = np.reshape(itr, [-1])
cit = len(evaldt)
out[idx:idx + cit] = evaldt[:]
idx += cit
out2 = np.sqrt(np.sum(np.square(out)))
out = np.divide(out, out2)
return out
def REGU(self, data):
rdata = data - self.mu
rdata = np.divide(rdata, self.sigma)
return rdata
def TRIM(self, data, nlarge):
absdata = np.abs(data)
large = heapq.nlargest(nlarge,
range(len(data)),
key=lambda x: absdata[x])
lst = np.zeros_like(data)
for itr in large:
lst[itr] = data[itr] / absdata[itr]
return lst
def FIG(self, tr):
wm = self.wmg.minhash(tr) # wm1 is of the type WeightedMinHash
vl = np.transpose(wm.hashvalues)
vl = vl[0]
return (vl.tolist())