How to use diffusion - 5 common examples

def test(self):
        """
        Creates Gauss kernels on the CPU/GPU and in numpy and compares these.
        """
        print('*** Running Test: ' + self.__class__.__name__ + ' function: ' + _getframe().f_code.co_name)
        # this test fails with mX = 9 and mY = 8 due to a bug in nvcc that needs more investigation
        # for now, skip this test to the build can pass.
        # for mY in [1, 2, 3, 8]:
        #     for mX in [1, 2, 3, 9]:
        for mY in [1, 2, 3, 9]:
            for mX in [1, 2, 3, 8]:
                print "Test case mX = %d and mY = %d." % (mX, mY) # Print parameters of the test case.
                op = Gauss2DOp(dimOut=[mY,mX], clear_cache=True)

                dataCPU = op.evaluate_c()
                dataNPY = gauss2DNp([mY,mX])
                assert np.allclose(dataCPU, dataNPY)

                if ops.local.cuda_enabled:
                    dataGPU = op.evaluate_cuda()
                    assert np.allclose(dataGPU, dataNPY)

def test(self):
        """
        Creates Gauss kernels on the CPU/GPU and in numpy and compares these.
        """
        print('*** Running Test: ' + self.__class__.__name__ + ' function: ' + _getframe().f_code.co_name)
        # this test fails with mX = 9 and mY = 8 due to a bug in nvcc that needs more investigation
        # for now, skip this test to the build can pass.
        # for mY in [1, 2, 3, 8]:
        #     for mX in [1, 2, 3, 9]:
        for mY in [1, 2, 3, 9]:
            for mX in [1, 2, 3, 8]:
                print "Test case mX = %d and mY = %d." % (mX, mY) # Print parameters of the test case.
                op = Gauss2DOp(dimOut=[mY,mX], clear_cache=True)

                dataCPU = op.evaluate_c()
                dataNPY = gauss2DNp([mY,mX])
                assert np.allclose(dataCPU, dataNPY)

                if ops.local.cuda_enabled:
                    dataGPU = op.evaluate_cuda()
                    assert np.allclose(dataGPU, dataNPY)

def search():
    n_query = len(queries)
    diffusion = Diffusion(np.vstack([queries, gallery]), args.cache_dir)
    offline = diffusion.get_offline_results(args.truncation_size, args.kd)
    features = preprocessing.normalize(offline, norm="l2", axis=1)
    scores = features[:n_query] @ features[n_query:].T
    ranks = np.argsort(-scores.todense())
    evaluate(ranks)

def search_old(gamma=3):
    diffusion = Diffusion(gallery, args.cache_dir)
    offline = diffusion.get_offline_results(args.truncation_size, args.kd)

    time0 = time.time()
    print('[search] 1) k-NN search')
    sims, ids = diffusion.knn.search(queries, args.kq)
    sims = sims ** gamma
    qr_num = ids.shape[0]

    print('[search] 2) linear combination')
    all_scores = np.empty((qr_num, args.truncation_size), dtype=np.float32)
    all_ranks = np.empty((qr_num, args.truncation_size), dtype=np.int)
    for i in tqdm(range(qr_num), desc='[search] query'):
        scores = sims[i] @ offline[ids[i]]
        parts = np.argpartition(-scores, args.truncation_size)[:args.truncation_size]
        ranks = np.argsort(-scores[parts])
        all_scores[i] = scores[parts][ranks]

def initialize(self, input_stream=None):
    
        # If no input file/stream specified, use default (or command line?)
        # (or default values?)
        if input_stream is None:
            input_stream = str(raw_input('Enter name of input file: '))
        
        # Open input file
        inputs = ModelParameterDictionary(input_stream)
    
        # Create grid
        self.grid = create_and_initialize_grid(inputs)
        
        # Create a diffusion component
        self.diffusion_component = diffusion.DiffusionComponent(self.grid)
        self.diffusion_component.initialize(input_stream)
        
        # Read parameters
        self.run_duration = inputs.get('RUN_DURATION', ptype=float)
        self.opt_netcdf_output = inputs.get('OPT_FILE_OUTPUT', ptype='bool')
        self.opt_display_output = inputs.get('OPT_DISPLAY_OUTPUT', ptype='bool')
        
        self.setup_output_timing(inputs)