def test_fit():
    """Checks whether `fit` method sets all attribute values correctly."""
    n_samples = 12
    n_features = 2
    n_estimators = 5
    rng = np.random.RandomState(42)
    X = rng.rand(n_samples, n_features)

    lshf = LSHForest(n_estimators=n_estimators)
    lshf.fit(X)

    # _input_array = X
    assert_array_equal(X, lshf._fit_X)
    # A hash function g(p) for each tree
    assert_equal(n_estimators, len(lshf.hash_functions_))
    # Hash length = 32
    assert_equal(32, lshf.hash_functions_[0].components_.shape[0])
    # Number of trees_ in the forest
    assert_equal(n_estimators, len(lshf.trees_))
    # Each tree has entries for every data point
    assert_equal(n_samples, len(lshf.trees_[0]))
    # Original indices after sorting the hashes
    assert_equal(n_estimators, len(lshf.original_indices_))
    # Each set of original indices in a tree has entries for every data point
    assert_equal(n_samples, len(lshf.original_indices_[0]))

    def single_batch(self, tweets):
        """Performs an approximate nearest neighbors search on tweets in the database
        passed to it. The database must be a list of tweets (text of the tweets only).
        
        Returns the indices of tweets with nearby neighbors (i.e. spam tweets).
        These indices correspond to indices within the batch of tweets fed to
        this function."""

        # Vectorize and fit tree:
        vect2 = CountVectorizer(stop_words = self.custom_stop_words)
        X2 = vect2.fit_transform(tweets)
        tree2 = LSHForest()
        tree2.fit(X2)

        # Build tree:
        n_neighbors = []
        neighbors_indices = []
        working_batch_size = len(tweets)
        for x in vect2.transform(tweets):
            if len(n_neighbors) % 100 == 0: print "%r tweets analyzed out of %r for this batch" % (len(n_neighbors), working_batch_size)
            # Only deal with tweets that are longer than 3 words.
            neighbors = tree2.radius_neighbors(x, radius = self.sensitivity)[1]
            if x.getnnz() > 2:
                n_neighbors.append(len(neighbors[0]))
                neighbors_indices.append(neighbors)
            else:
                n_neighbors.append(1)
                neighbors_indices.append(np.array([np.array([0])]))

        neighbors_indices = [x for x in range(len(neighbors_indices)) if len(neighbors_indices[x][0]) > 2]

        return neighbors_indices

class EmbeddingNetworkBuilder:
    """ Basically a wrapper around sklearns LSH forest """

    def __init__(self, lsh_init=None):
        if lsh_init == None:
            self._lsh_forest = LSHForest(n_estimators=25, n_candidates=1000)
        else:
            self._lsh_forest = lsh_init 
        self.iw = None
        self.m = None

    def fit_lsh_forest(self, embedding):
        self._lsh_forest.fit(embedding.m)
        self._embedding = embedding

    def extract_nn_network(self, nn=20):
        dir_graph_mat = self._lsh_forest.kneighbors_graph(X=self._embedding.m, n_neighbors=nn+1)
        return dir_graph_mat

    def make_undirected(self, dir_graph_mat):
        nodes = set(range(dir_graph_mat.shape[0]))
        edges = set([])
        for node_i in dir_graph_mat.shape[0]:
            for node_j in dir_graph_mat[node_i].nonzero()[1]:
                edges.add((node_i, node_j))
        return nodes, edges

    def get_forest(self):
        return self._lsh_forest
    
    def get_node_to_word(self):
        return self.iw

    def single_batch(self, tweets):
        """Performs an approximate nearest neighbors search on tweets in the database
        passed to it. The database must be a list of tweets (text of the tweets only).
        
        Returns the indices of tweets with nearby neighbors (i.e. spam tweets).
        These indices correspond to indices within the batch of tweets fed to
        this function."""

        # Vectorize and fit tree:
        vect2 = CountVectorizer(stop_words = self.common_twitter_handles)
        X2 = vect2.fit_transform(tweets)
        tree2 = LSHForest()
        tree2.fit(X2)

        # Build tree:
        n_neighbors = []
        neighbors_indices = []
        for x in vect2.transform(tweets):
            if len(n_neighbors) % 100 == 0: print "%r tweets analyzed out of %r for this batch" % (len(n_neighbors), self.batch_size)
            neighbors = tree2.radius_neighbors(x, radius = .4)[1]
            n_neighbors.append(len(neighbors[0]))
            neighbors_indices.append(neighbors)

        neighbors_indices = [x for x in range(len(neighbors_indices)) if len(neighbors_indices[x][0]) > 2]

        return neighbors_indices

    def get_nearest_neighbor_iterable(self, graphlist, start_graphs, start_is_subset=True):

        # vectorize all
        graphlist= list(graphlist)
        graphlist_ = copy.deepcopy(graphlist)
        X = self.vectorizer.transform_single(graphlist_)


        start_graphs= list(start_graphs)
        graphlist_= copy.deepcopy(start_graphs)
        Y = self.vectorizer.transform_single(graphlist_)
        
        
        forest = LSHForest()
        forest.fit(X)
        #http://scikit-learn.org/stable/modules/neighbors.html
        distances, indices = forest.kneighbors(Y, n_neighbors=2)

        # we just assume that this is short...
        index = 0
        if start_is_subset:
            index += 1
        
        #matches= ( X_index ,Y_index, distance  )
        matches = [(indices[i, index], i, distances[i, index]) for i in range(len(indices))]
        matches.sort()

        # this looks super confusing....
        #for index, graph in enumerate(selection_iterator(graphlist, [a[0] for a in matches])):
        #    yield ((graph, start_graphs[matches[index][1]], X[matches[index][0]]))
        # so i wrote this:,,, you may even get rid of the matches variable i think.. and use indices directly
        for Xi,Yi,dist in matches:
            yield ((start_graphs[Yi],graphlist[Xi],X[Xi]))

def test_hash_functions():
    """Checks randomness of hash functions.

    Variance and mean of each hash function (projection vector)
    should be different from flattened array of hash functions.
    If hash functions are not randomly built (seeded with
    same value), variances and means of all functions are equal.
    """
    n_samples = 12
    n_features = 2
    n_estimators = 5
    rng = np.random.RandomState(42)
    X = rng.rand(n_samples, n_features)

    lshf = LSHForest(n_estimators=n_estimators,
                     random_state=rng.randint(0, np.iinfo(np.int32).max))
    lshf.fit(X)

    hash_functions = []
    for i in range(n_estimators):
        hash_functions.append(lshf.hash_functions_[i].components_)

    for i in range(n_estimators):
        assert_not_equal(np.var(hash_functions),
                         np.var(lshf.hash_functions_[i].components_))

    for i in range(n_estimators):
        assert_not_equal(np.mean(hash_functions),
                         np.mean(lshf.hash_functions_[i].components_))

def search_neighbors(request):
	designs = Design.objects.all()

	image_list = []
	for design in designs:
		image_list.append(str(design.uid) + ".png")

	d_geometry = settings.D_GEOMETRY
	designed_images = np.empty((len(image_list), d_geometry[0]*d_geometry[1]*3), dtype="float32")
	for i in range(len(image_list)):
		designed_images[i] = img2numpy_arr(settings.DESIGN_PATH + image_list[i]).reshape(d_geometry[0]*d_geometry[1]*3)
	designed_images /= 255
	
	lshf = LSHForest(random_state=42)
	lshf.fit(designed_images) 

	num = int(request.GET['num'])
	input_fname = str(request.GET['input'])
	input_image = img2numpy_arr(settings.DESIGN_PATH + input_fname)
	input_image = input_image.reshape(1, -1)/255
	_, indices = lshf.kneighbors(input_image, n_neighbors=num)

	similar_images = []
	for i in list(indices.reshape(-1)):
		similar_images.append({ 
			"image": str(designs[i].uid) + ".png", 
			"text": str(designs[i].history_text), 
			"like": int(designs[i].like),
			"filtered": str(designs[i].filtered)
		})

	return JsonResponse({
		"results": similar_images
	})

def build_index(data, n_estimators=20, n_candidates=100, n_neighbors=10, seed=0):
    lshf = LSHForest(n_estimators=n_estimators, n_candidates=n_candidates,
                     n_neighbors=n_neighbors, random_state=seed)
    t0 = time()
    lshf.fit(data)
    duration = time() - t0
    return lshf, duration

    def get_heap_and_forest(self, griter, k):
        '''
        so we create the heap and the forest...
        heap is (dist to hyperplane, count, graph)
        and the forest ist just a nearest neighbor from sklearn
        '''

        graphs = list(griter)
        graphs2 = copy.deepcopy(graphs)
        # transform doess mess up the graph objects
        X = self.vectorizer.transform(graphs)

        forest = LSHForest()
        forest.fit(X)
        print 'got forest'

        heap = []
        for vector, graph in zip(X, graphs2):
            graph2 = nx.Graph(graph)
            heapq.heappush(heap, (
                self.sampler.estimator.predict_proba(self.sampler.vectorizer.transform_single(graph2))[0][1],
                # score ~ dist from hyperplane
                k + 1,  # making sure that the counter is high so we dont output the startgraphz at the end
                graph))  # at last the actual graph

        print 'got heap'
        distances, unused = forest.kneighbors(X, n_neighbors=2)
        distances = [a[1] for a in distances]  # the second element should be the dist we want
        avg_dist = distances[len(distances) / 2]  # sum(distances)/len(distances)
        print 'got dist'

        return heap, forest, avg_dist

def test_neighbors_accuracy_with_n_estimators():
    # Checks whether accuracy increases as `n_estimators` increases.
    n_estimators = np.array([1, 10, 100])
    n_samples = 100
    n_features = 10
    n_iter = 10
    n_points = 5
    rng = np.random.RandomState(42)
    accuracies = np.zeros(n_estimators.shape[0], dtype=float)
    X = rng.rand(n_samples, n_features)

    for i, t in enumerate(n_estimators):
        lshf = LSHForest(n_candidates=500, n_estimators=t)
        ignore_warnings(lshf.fit)(X)
        for j in range(n_iter):
            query = X[rng.randint(0, n_samples)].reshape(1, -1)
            neighbors = lshf.kneighbors(query, n_neighbors=n_points,
                                        return_distance=False)
            distances = pairwise_distances(query, X, metric='cosine')
            ranks = np.argsort(distances)[0, :n_points]

            intersection = np.intersect1d(ranks, neighbors).shape[0]
            ratio = intersection / float(n_points)
            accuracies[i] = accuracies[i] + ratio

        accuracies[i] = accuracies[i] / float(n_iter)
    # Sorted accuracies should be equal to original accuracies
    assert_true(np.all(np.diff(accuracies) >= 0),
                msg="Accuracies are not non-decreasing.")
    # Highest accuracy should be strictly greater than the lowest
    assert_true(np.ptp(accuracies) > 0,
                msg="Highest accuracy is not strictly greater than lowest.")

def text_hist():
    """
    Calculate histogram of text of images
    """
    with open('data/sift_names.pkl', 'r') as f:
        names = cPickle.load(f)
    with open('data/sift_hist.pkl', 'r') as f:
        sift_hists = cPickle.load(f)
    filenames = []
    for name in names:
        name = name.replace('img', 'descr')
        name = name.replace('.jpg', '.txt')
        filenames.append('shopping/images/' + name)
    vectorizer = CountVectorizer(input='filename', token_pattern="(?u)"+'\w+', ngram_range=(1, 1), min_df=2)
    xall_transformed = vectorizer.fit_transform(filenames).tocsr()
    preprocessing.normalize(xall_transformed, copy=False)

    lamb = .5
    hists = scipy.sparse.hstack([xall_transformed * lamb, sift_hists * (1-lamb)]).toarray()
    preprocessing.normalize(hists, copy=False)
    model = LSHForest()
    model.fit(hists)
    with open('data/text_hist.pkl', 'w') as f:
        cPickle.dump(xall_transformed, f)
    with open('data/vectorizer.pkl', 'w') as f:
        cPickle.dump(vectorizer, f)
    with open('data/lshforest_combine.pkl', 'w') as f:
        cPickle.dump(model, f)

def test_distances():
    """Checks whether returned neighbors are from closest to farthest."""
    n_samples = 12
    n_features = 2
    n_iter = 10
    rng = np.random.RandomState(42)
    X = rng.rand(n_samples, n_features)

    lshf = LSHForest()
    lshf.fit(X)

    for i in range(n_iter):
        n_neighbors = rng.randint(0, n_samples)
        query = X[rng.randint(0, n_samples)]
        distances, neighbors = lshf.kneighbors(query,
                                               n_neighbors=n_neighbors,
                                               return_distance=True)
        # Returned neighbors should be from closest to farthest.
        assert_true(np.all(np.diff(distances[0]) >= 0))

        mean_dist = np.mean(pairwise_distances(query, X, metric='cosine'))
        distances, neighbors = lshf.radius_neighbors(query,
                                                     radius=mean_dist,
                                                     return_distance=True)
        assert_true(np.all(np.diff(distances[0]) >= 0))

 def create_tree(self,listNames,variableName):
     #LSHForest. only once for the main database
     lshf = LSHForest(n_estimators=50,n_candidates=500)
     TF, tfidfs = self.create_TDIDF(self.tokenize(listNames))
     lshf.fit(tfidfs)        
     pickle.dump(lshf,open("{0}/{1}_lshf.dump".format(self.folderSaveData,variableName),"wb+"))
     pickle.dump(listNames,open("{0}/{1}_listNames.dump".format(self.folderSaveData,variableName),"wb+"))
     pickle.dump(TF,open("{0}/{1}_TF.dump".format(self.folderSaveData,variableName),"wb+"))

def test_radius_neighbors_boundary_handling():
    X = [[0.999, 0.001], [0.5, 0.5], [0, 1.], [-1., 0.001]]
    n_points = len(X)

    # Build an exact nearest neighbors model as reference model to ensure
    # consistency between exact and approximate methods
    nnbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)

    # Build a LSHForest model with hyperparameter values that always guarantee
    # exact results on this toy dataset.
    lsfh = LSHForest(min_hash_match=0, n_candidates=n_points,
                     random_state=42).fit(X)

    # define a query aligned with the first axis
    query = [[1., 0.]]

    # Compute the exact cosine distances of the query to the four points of
    # the dataset
    dists = pairwise_distances(query, X, metric='cosine').ravel()

    # The first point is almost aligned with the query (very small angle),
    # the cosine distance should therefore be almost null:
    assert_almost_equal(dists[0], 0, decimal=5)

    # The second point form an angle of 45 degrees to the query vector
    assert_almost_equal(dists[1], 1 - np.cos(np.pi / 4))

    # The third point is orthogonal from the query vector hence at a distance
    # exactly one:
    assert_almost_equal(dists[2], 1)

    # The last point is almost colinear but with opposite sign to the query
    # therefore it has a cosine 'distance' very close to the maximum possible
    # value of 2.
    assert_almost_equal(dists[3], 2, decimal=5)

    # If we query with a radius of one, all the samples except the last sample
    # should be included in the results. This means that the third sample
    # is lying on the boundary of the radius query:
    exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1)
    approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1)

    assert_array_equal(np.sort(exact_idx[0]), [0, 1, 2])
    assert_array_equal(np.sort(approx_idx[0]), [0, 1, 2])
    assert_array_almost_equal(np.sort(exact_dists[0]), dists[:-1])
    assert_array_almost_equal(np.sort(approx_dists[0]), dists[:-1])

    # If we perform the same query with a slightly lower radius, the third
    # point of the dataset that lay on the boundary of the previous query
    # is now rejected:
    eps = np.finfo(np.float64).eps
    exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1 - eps)
    approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1 - eps)

    assert_array_equal(np.sort(exact_idx[0]), [0, 1])
    assert_array_equal(np.sort(approx_idx[0]), [0, 1])
    assert_array_almost_equal(np.sort(exact_dists[0]), dists[:-2])
    assert_array_almost_equal(np.sort(approx_dists[0]), dists[:-2])

class LHSForestEngine:

    def __init__(self):
        self.engine = LSHForest(random_state=42)
        self.name = "LHS"

    def fit(self, data):
        self.engine.fit(data)

    def dist(self, data):
        distances, indices = self.engine.kneighbors(data, n_neighbors=1)
        return distances.ravel()

 def calculate_duplication_number(self,text_list):
     print "length is ", len(text_list)
     tf_vectorizer = CountVectorizer(stop_words=None,analyzer='word',ngram_range=(5,5))
     #print text_list
     tf = tf_vectorizer.fit_transform(text_list)
     #print tf_vectorizer.get_feature_names()
     print tf[0]
     #print tf[123]
     lshf = LSHForest()
     #print tf
     lshf.fit(tf)
     distance,index = lshf.kneighbors(tf,n_neighbors=1)
     print distance, index

def startQuery():
    while True:

      try:
          ipt = raw_input('Directory of query:')
      except ImportError:
          print 'invalid type'
      else:
          query = ipt
      if query == 'exit()':
          break

      

    

      print 'loading query...'
      try:
          token = get_tokens_by_dir(query)
      except IOError:
          print 'invalid file name'
      else:
##########################################query preprocessing
           print 'query pre-processing...'
           stopped_tokens = [i for i in token if not i in en_stop]
           p_stemmer = PorterStemmer()
           stemed_tokens = []
           for i in stopped_tokens:
               try:
                   temp_token = str(p_stemmer.stem(i))
                   stemed_tokens.append(temp_token)
               except IndexError:
                   pass
           tokens = [stemed_tokens]
######################################################################################
           dictionary_new = corpora.Dictionary(tokens)
           corpus_new = [dictionary_new.doc2bow(text) for text in tokens]
           QUERY_TOPIC = np.zeros([1,num_topic]) ## topic vector for query

           new_topics = LDA[corpus_new]


           for i in new_topics[0]:
               print(i)
               QUERY_TOPIC[0,i[0]] = i[1] ##assign new topics to query doc-topic matrix

           print 'fetching results for you...'
           lshf = LSHForest(random_state=42)
           lshf.fit(DOC_TOPICS) ##fit the local sensitive hash forest with training data POINT_SET
           dist,indices=lshf.kneighbors(QUERY_TOPIC,n_neighbors=20)
           print indices

    def fit_model(self, model_type='brute', params=None):
        '''
        fits model operating under the assumption that there's a model already built
        '''

        if model_type == 'brute':
            self.model = NearestNeighbors(algorithm='brute', **params)
        elif model_type == 'lsh':
            self.model = LSHForest( **params)
        # elif model_type == 'annoy':
        #     self.model = Annoy(**params)

        self.model.fit(self.vector_space)
        print self.model        

def fit_lshf(data):
    logger.info('Fitting  LSHForest...')
    from sklearn.neighbors import LSHForest
    lshf = LSHForest(
        n_estimators=20,
        min_hash_match=4,
        n_candidates=200,
        n_neighbors=2,
        radius=1.0,
        radius_cutoff_ratio=0.9,
        random_state=None,
    )
    lshf.fit(data)
    return lshf

 def __init__(self):
     self.unknown = ''
     self.same_person_num = 1
     self.has_cal_dist = []
     self.NeighbourNum = 10
     # 如果管理员加载图片, 把图片放到all_pic_data_folder下指定人的目录(图片文件和特征文件的文件名相同)
     self.all_pic_feature_data_folder = '/data/liubo/face/research_feature_self'     # 研究院的模型直接存储特征
     # 保存图片可以方便以后查看效果, 方便前端显示, 也方便管理员进行标注
     self.all_pic_data_folder = '/data/liubo/face/research_self'
     if not os.path.exists(self.all_pic_data_folder):
         os.makedirs(self.all_pic_data_folder)
     if not os.path.exists(self.all_pic_feature_data_folder):
         os.makedirs(self.all_pic_feature_data_folder)
     self.n_neighbors = 10
     self.lshf = LSHForest(n_estimators=20, n_candidates=200, n_neighbors=self.n_neighbors)
     self.all_labels = []
     self.all_pic_feature = []
     self.same_pic_id = 2
     self.must_be_same_id = 1
     self.must_be_not_same_id = 0
     self.maybe_same_id = 3
     self.new_person_str = 'new_person_'
     self.current_new_person_id = self.find_current_new_person_id()
     self.must_same_str = '_Must_Same'
     self.maybe_same_str = '_Maybe_same'
     self.load_time = time.time()
     self.user_count = {}
     self.upper_threshold = upper_verif_threshold
     self.lower_threshold = lower_verif_threshold
     self.same_pic_threshold = same_pic_threshold
     self.trans_dic = {self.same_pic_id: 'same_pic', self.must_be_same_id: 'must_same_id',
                       self.must_be_not_same_id: 'must_not_same_id', self.maybe_same_id: 'maybe_same_id'}
     self.nearest = deque(maxlen=nearest_num)
     self.verification_same_person = 0