def test_vector_loaded():
space_type = 'cosinesimil'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '.index'
index = nmslib_vector.init(space_type, space_param, method_name,
nmslib_vector.DataType.VECTOR,
nmslib_vector.DistType.FLOAT)
for id, data in enumerate(read_data('sample_dataset.txt')):
nmslib_vector.addDataPoint(index, id, data)
query_time_param = ['initSearchAttempts=3']
nmslib_vector.loadIndex(index, index_name)
print "The index %s is loaded" % index_name
nmslib_vector.setQueryTimeParams(index, query_time_param)
print 'Query time parameters are set'
print "Results for the loaded index"
k = 2
for idx, data in enumerate(read_data('sample_queryset.txt')):
print idx, nmslib_vector.knnQuery(index, k, data)
nmslib_vector.freeIndex(index)
def test_string_fresh():
DATA_STRS = [
"xyz", "beagcfa", "cea", "cb", "d", "c", "bdaf", "ddcd", "egbfa", "a",
"fba", "bcccfe", "ab", "bfgbfdc", "bcbbgf", "bfbb"
]
QUERY_STRS = ["abc", "def", "ghik"]
space_type = 'leven'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '.index'
index = nmslib_vector.init(space_type, space_param, method_name,
nmslib_vector.DataType.STRING,
nmslib_vector.DistType.INT)
for id, data in enumerate(DATA_STRS):
nmslib_vector.addDataPoint(index, id, data)
index_param = ['NN=17', 'initIndexAttempts=3', 'indexThreadQty=4']
query_time_param = ['initSearchAttempts=3']
nmslib_vector.createIndex(index, index_param)
nmslib_vector.setQueryTimeParams(index, query_time_param)
print 'Query time parameters are set'
print "Results for the freshly created index:"
k = 2
for idx, data in enumerate(QUERY_STRS):
print idx, nmslib_vector.knnQuery(index, k, data)
nmslib_vector.saveIndex(index, index_name)
print "The index %s is saved" % index_name
nmslib_vector.freeIndex(index)
def test_vector_loaded():
space_type = 'cosinesimil'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '.index'
index = nmslib_vector.init(
space_type,
space_param,
method_name,
nmslib_vector.DataType.VECTOR,
nmslib_vector.DistType.FLOAT)
for id, data in enumerate(read_data('sample_dataset.txt')):
nmslib_vector.addDataPoint(index, id, data)
query_time_param = ['initSearchAttempts=3']
nmslib_vector.loadIndex(index, index_name)
print "The index %s is loaded" % index_name
nmslib_vector.setQueryTimeParams(index,query_time_param)
print 'Query time parameters are set'
print "Results for the loaded index"
k = 2
for idx, data in enumerate(read_data('sample_queryset.txt')):
print idx, nmslib_vector.knnQuery(index, k, data)
nmslib_vector.freeIndex(index)
def find_to_k(query_fp, k, nmslib_vector, category_index):
t1 = time()
if type(query_fp) == list:
color = query_fp
elif type(query_fp) == dict:
color = query_fp['color']
else:
print('bad fp')
return
top_k = nmslib_vector.knnQuery(category_index, k, color)
t2 = time()
print('find took = %s' % str(t2 - t1))
return top_k
def test_string_fresh():
DATA_STRS = ["xyz", "beagcfa", "cea", "cb",
"d", "c", "bdaf", "ddcd",
"egbfa", "a", "fba", "bcccfe",
"ab", "bfgbfdc", "bcbbgf", "bfbb"
]
QUERY_STRS = ["abc", "def", "ghik"]
space_type = 'leven'
space_param = []
method_name = 'small_world_rand'
index_name = method_name + '.index'
index = nmslib_vector.init(
space_type,
space_param,
method_name,
nmslib_vector.DataType.STRING,
nmslib_vector.DistType.INT)
for id, data in enumerate(DATA_STRS):
nmslib_vector.addDataPoint(index, id, data)
index_param = ['NN=17', 'initIndexAttempts=3', 'indexThreadQty=4']
query_time_param = ['initSearchAttempts=3']
nmslib_vector.createIndex(index, index_param)
nmslib_vector.setQueryTimeParams(index, query_time_param)
print 'Query time parameters are set'
print "Results for the freshly created index:"
k = 2
for idx, data in enumerate(QUERY_STRS):
print idx, nmslib_vector.knnQuery(index, k, data)
nmslib_vector.saveIndex(index, index_name)
print "The index %s is saved" % index_name
nmslib_vector.freeIndex(index)
def knn_query(self, index, vector):
assert index[1] == vector.shape, repr((index[1], vector.shape))
return nmslib_vector.knnQuery(index[0], index[2], list(vector))
def query(self, v, n):
import nmslib_vector
return nmslib_vector.knnQuery(self._index, n, v.tolist())
