Esempio n. 1
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    def testSegment(self):
        # TODO
        return True
        sensor_params = {"width": 32, "height": 32, "background": 1, "mode": "bw"}
        net_params = [
            {
                "nodeCloning": True,
                "size": [2, 2],
                "overlap": [0, 0],
                # Spatial pooler
                "maxCoincidenceCount": 128,
                "spatialPoolerAlgorithm": "gaussian",
                "sigma": 1,
                "maxDistance": 0.1,
                # Temporal pooler
                "requestedGroupsCount": 20,
                "temporalPoolerAlgorithm": "maxProp",
                "transitionMemory": 4,
            }
        ]

        learning_params = [
            # Level 0
            {
                "sp":
                # Spatial pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 100,
                },
                "tp":
                # Temporal pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 1000,
                },
            }
        ]
        train_data = "data/pictures-subset/train"

        sensor = ImageSensor(
            width=sensor_params["width"],
            height=sensor_params["height"],
            background=sensor_params["background"],
            mode=sensor_params["mode"],
        )
        net = HTM(sensor, verbose=False)
        net.create_network(net_params)
        sensor.loadMultipleImages(train_data)

        net.learn(learning_params)

        sensor.clearImageList()
        sensor.loadSingleImage("data/test_clean.png")

        data = sensor.compute()
        im_clean, cat = data["data"], data["category"]
        patch_size = net.patch_size

        im_clean_fw = net.infer(utils.extract_patches(im_clean, patch_size), merge_output=True)

        weights = np.asarray(net.segment(im_clean_fw))
Esempio n. 2
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    def testSaveAndLoad(self):
        sensor_params = {"width": 32, "height": 32, "background": 1, "mode": "bw"}
        net_params = [
            {
                "nodeCloning": True,
                "size": [2, 2],
                "overlap": [0, 0],
                # Spatial pooler
                "maxCoincidenceCount": 128,
                "spatialPoolerAlgorithm": "gaussian",
                "sigma": 1,
                "maxDistance": 0.1,
                # Temporal pooler
                "requestedGroupsCount": 20,
                "temporalPoolerAlgorithm": "maxProp",
                "transitionMemory": 4,
            }
        ]

        learning_params = [
            # Level 0
            {
                "sp":
                # Spatial pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 100,
                },
                "tp":
                # Temporal pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 1000,
                },
            }
        ]
        train_data = "data/pictures-subset/train"

        sensor = ImageSensor(
            width=sensor_params["width"],
            height=sensor_params["height"],
            background=sensor_params["background"],
            mode=sensor_params["mode"],
        )
        net = HTM(sensor, verbose=False)
        net.create_network(net_params)
        sensor.loadMultipleImages(train_data)

        net.learn(learning_params)
        net_filename = "network.pkl"
        net.save(filename=net_filename)

        self.assertTrue(os.path.exists(net_filename))
        newNet = HTM()
        newNet.load(filename=net_filename)
        os.unlink(net_filename)

        newNet.sensor.clearImageList()
        newNet.sensor.setParameter("explorer", "Flash")

        self.assertListEqual(net.patch_size, newNet.patch_size)
Esempio n. 3
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    def testMultipleLevelInference(self):
        return
        from datasets import DatasetConfig

        dataset = DatasetConfig().load("pictures-subset")

        train_data = dataset["test_data_path"]  # dataset['train_data_path']
        test_data = dataset["test_data_path"]

        sensor_params = {
            "width": dataset["image_width"],
            "height": dataset["image_height"],
            "background": dataset["image_background"],
            "mode": dataset["image_mode"],
        }

        net_params = [
            # Level 0
            {
                "nodeCloning": True,
                "size": [4, 4],
                "overlap": [0, 0],
                # Spatial pooler
                "maxCoincidenceCount": 64,
                "spatialPoolerAlgorithm": "gaussian",
                "sigma": 1,
                "maxDistance": 0.1,
                # Temporal pooler
                "requestedGroupsCount": 20,
                "temporalPoolerAlgorithm": "sumProp",
                "transitionMemory": 4,
            },
            # Level 1
            {
                "nodeCloning": True,
                "size": [2, 2],
                "overlap": [0, 0],
                # Spatial pooler
                "maxCoincidenceCount": 128,
                "spatialPoolerAlgorithm": "product",
                "sigma": 1,
                "maxDistance": 0.1,
                # Temporal pooler
                "requestedGroupsCount": 10,
                "temporalPoolerAlgorithm": "sumProp",
                "transitionMemory": 8,
            },
            # Level 2
            {
                "nodeCloning": True,
                "size": [1],
                "overlap": [0, 0],
                # Spatial pooler
                "maxCoincidenceCount": 128,
                "spatialPoolerAlgorithm": "product",
                "sigma": 1,
                "maxDistance": 0.1,
                # Temporal pooler
                "requestedGroupsCount": 20,
                "temporalPoolerAlgorithm": "sumProp",
                "transitionMemory": 10,
            },
            # Level 3
            # {
            #     'nodeCloning': True,
            #     'size': [1],
            #     'overlap': [0, 0],
            #     # Spatial pooler
            #         'maxCoincidenceCount': 128,
            #         'spatialPoolerAlgorithm': 'gaussian',
            #         'sigma': 1,
            #         'maxDistance': 0.1,
            #     # Temporal pooler
            #         'requestedGroupsCount': 30,
            #         'temporalPoolerAlgorithm': 'maxProp',
            #         'transitionMemory': 10,
            # }
        ]

        learning_params = [
            # Level 0
            {
                "sp":
                # Spatial pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 300,
                },
                "tp":
                # Temporal pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 10000,
                },
            },
            # Level 1
            {
                "sp":
                # Spatial pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 200,
                },
                "tp":
                # Temporal pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 5000,
                },
            },
            # Level 2
            {
                "sp":
                # Spatial pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 200,
                },
                "tp":
                # Temporal pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 5000,
                },
            },
            # Level 3
            {
                "sp":
                # Spatial pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 200,
                },
                "tp":
                # Temporal pooler
                {
                    "explorer": ["RandomSweep", {"sweepOffObject": False, "sweepDirections": "all"}],
                    "numIterations": 5000,
                },
            },
        ]
        train_data = "data/pictures-subset/train"
        test_data = "data/pictures-subset/test"

        sensor = ImageSensor(
            width=sensor_params["width"],
            height=sensor_params["height"],
            background=sensor_params["background"],
            mode=sensor_params["mode"],
        )
        net = HTM(sensor, verbose=False)
        net.create_network(net_params)
        sensor.loadMultipleImages(train_data)

        net.learn(learning_params)

        # getting testing data
        sensor.clearImageList()
        sensor.loadMultipleImages(test_data)
        sensor.setParameter("explorer", "Flash")

        import matplotlib.pyplot as plt
        import matplotlib.cm as cm

        for i in range(40):
            data = sensor.compute()
            im, cat = data["data"], data["category"]
            print(cat)
            patterns = utils.extract_patches(im, net.patch_size)
            if cat == 0:
                plt.plot(net.infer(patterns), color="b")
            else:
                plt.plot(net.infer(patterns), color="r")

        plt.show()
Esempio n. 4
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    def testSegment(self):
        # TODO
        return True
        sensor_params = {
            'width': 32,
            'height': 32,
            'background': 1,
            'mode': 'bw'
        }
        net_params = [{
            'nodeCloning': True,
            'size': [2, 2],
            'overlap': [0, 0],

            # Spatial pooler
            'maxCoincidenceCount': 128,
            'spatialPoolerAlgorithm': 'gaussian',
            'sigma': 1,
            'maxDistance': 0.1,

            # Temporal pooler
            'requestedGroupsCount': 20,
            'temporalPoolerAlgorithm': 'maxProp',
            'transitionMemory': 4,
        }]

        learning_params = [
            # Level 0
            {
                'sp':
                # Spatial pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    100
                },
                'tp':
                # Temporal pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    1000
                },
            },
        ]
        train_data = 'data/pictures-subset/train'

        sensor = ImageSensor(width=sensor_params['width'],
                             height=sensor_params['height'],
                             background=sensor_params['background'],
                             mode=sensor_params['mode'])
        net = HTM(sensor, verbose=False)
        net.create_network(net_params)
        sensor.loadMultipleImages(train_data)

        net.learn(learning_params)

        sensor.clearImageList()
        sensor.loadSingleImage('data/test_clean.png')

        data = sensor.compute()
        im_clean, cat = data['data'], data['category']
        patch_size = net.patch_size

        im_clean_fw = net.infer(utils.extract_patches(im_clean, patch_size),
                                merge_output=True)

        weights = np.asarray(net.segment(im_clean_fw))
Esempio n. 5
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    def testSaveAndLoad(self):
        sensor_params = {
            'width': 32,
            'height': 32,
            'background': 1,
            'mode': 'bw'
        }
        net_params = [{
            'nodeCloning': True,
            'size': [2, 2],
            'overlap': [0, 0],

            # Spatial pooler
            'maxCoincidenceCount': 128,
            'spatialPoolerAlgorithm': 'gaussian',
            'sigma': 1,
            'maxDistance': 0.1,

            # Temporal pooler
            'requestedGroupsCount': 20,
            'temporalPoolerAlgorithm': 'maxProp',
            'transitionMemory': 4,
        }]

        learning_params = [
            # Level 0
            {
                'sp':
                # Spatial pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    100
                },
                'tp':
                # Temporal pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    1000
                },
            },
        ]
        train_data = 'data/pictures-subset/train'

        sensor = ImageSensor(width=sensor_params['width'],
                             height=sensor_params['height'],
                             background=sensor_params['background'],
                             mode=sensor_params['mode'])
        net = HTM(sensor, verbose=False)
        net.create_network(net_params)
        sensor.loadMultipleImages(train_data)

        net.learn(learning_params)
        net_filename = 'network.pkl'
        net.save(filename=net_filename)

        self.assertTrue(os.path.exists(net_filename))
        newNet = HTM()
        newNet.load(filename=net_filename)
        os.unlink(net_filename)

        newNet.sensor.clearImageList()
        newNet.sensor.setParameter('explorer', 'Flash')

        self.assertListEqual(net.patch_size, newNet.patch_size)
Esempio n. 6
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    def testMultipleLevelInference(self):
        return
        from datasets import DatasetConfig

        dataset = DatasetConfig().load('pictures-subset')

        train_data = dataset['test_data_path']  #dataset['train_data_path']
        test_data = dataset['test_data_path']

        sensor_params = {
            'width': dataset['image_width'],
            'height': dataset['image_height'],
            'background': dataset['image_background'],
            'mode': dataset['image_mode']
        }

        net_params = [
            # Level 0
            {
                'nodeCloning': True,
                'size': [4, 4],
                'overlap': [0, 0],

                # Spatial pooler
                'maxCoincidenceCount': 64,
                'spatialPoolerAlgorithm': 'gaussian',
                'sigma': 1,
                'maxDistance': 0.1,

                # Temporal pooler
                'requestedGroupsCount': 20,
                'temporalPoolerAlgorithm': 'sumProp',
                'transitionMemory': 4,
            },
            # Level 1
            {
                'nodeCloning': True,
                'size': [2, 2],
                'overlap': [0, 0],

                # Spatial pooler
                'maxCoincidenceCount': 128,
                'spatialPoolerAlgorithm': 'product',
                'sigma': 1,
                'maxDistance': 0.1,

                # Temporal pooler
                'requestedGroupsCount': 10,
                'temporalPoolerAlgorithm': 'sumProp',
                'transitionMemory': 8,
            },
            # Level 2
            {
                'nodeCloning': True,
                'size': [1],
                'overlap': [0, 0],

                # Spatial pooler
                'maxCoincidenceCount': 128,
                'spatialPoolerAlgorithm': 'product',
                'sigma': 1,
                'maxDistance': 0.1,

                # Temporal pooler
                'requestedGroupsCount': 20,
                'temporalPoolerAlgorithm': 'sumProp',
                'transitionMemory': 10,
            },
            # Level 3
            # {
            #     'nodeCloning': True,
            #     'size': [1],
            #     'overlap': [0, 0],

            #     # Spatial pooler
            #         'maxCoincidenceCount': 128,
            #         'spatialPoolerAlgorithm': 'gaussian',
            #         'sigma': 1,
            #         'maxDistance': 0.1,

            #     # Temporal pooler
            #         'requestedGroupsCount': 30,
            #         'temporalPoolerAlgorithm': 'maxProp',
            #         'transitionMemory': 10,
            # }
        ]

        learning_params = [
            # Level 0
            {
                'sp':
                # Spatial pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    300
                },
                'tp':
                # Temporal pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    10000
                },
            },
            # Level 1
            {
                'sp':
                # Spatial pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    200
                },
                'tp':
                # Temporal pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    5000
                },
            },
            # Level 2
            {
                'sp':
                # Spatial pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    200
                },
                'tp':
                # Temporal pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    5000
                },
            },
            # Level 3
            {
                'sp':
                # Spatial pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    200
                },
                'tp':
                # Temporal pooler
                {
                    'explorer': [
                        'RandomSweep', {
                            'sweepOffObject': False,
                            'sweepDirections': 'all'
                        }
                    ],
                    'numIterations':
                    5000
                },
            }
        ]
        train_data = 'data/pictures-subset/train'
        test_data = 'data/pictures-subset/test'

        sensor = ImageSensor(width=sensor_params['width'],
                             height=sensor_params['height'],
                             background=sensor_params['background'],
                             mode=sensor_params['mode'])
        net = HTM(sensor, verbose=False)
        net.create_network(net_params)
        sensor.loadMultipleImages(train_data)

        net.learn(learning_params)

        # getting testing data
        sensor.clearImageList()
        sensor.loadMultipleImages(test_data)
        sensor.setParameter('explorer', 'Flash')

        import matplotlib.pyplot as plt
        import matplotlib.cm as cm

        for i in range(40):
            data = sensor.compute()
            im, cat = data['data'], data['category']
            print(cat)
            patterns = utils.extract_patches(im, net.patch_size)
            if cat == 0:
                plt.plot(net.infer(patterns), color='b')
            else:
                plt.plot(net.infer(patterns), color='r')

        plt.show()