def entrance(request): arguments = [ { 'argument_type': 'div', 'argument_name': 'Language:', 'argument_id': 'result', }, { 'argument_type': 'button-group', 'argument_items': [{'button_name': 'recognize'}, {'button_name': 'clear' }] }] properties = { 'title' : 'Language Detection', 'template': {'type': 'text', 'caption': 'Enter a sentence to classify:', 'description': read_demo_description.read_description(__file__)}, 'panels': [ { 'panel_name': 'arguments', 'panel_label': 'Dashboard', 'panel_property': arguments, }, { 'panel_name': 'about', 'panel_label': 'About', 'panel_property': 'Developed by <a target="_blank" href="https://github.com/van51">van51</a>', } ] } return render_to_response("application/language_detect.html", properties, context_instance = RequestContext(request))
def entrance(request): arguments = [ { 'argument_type': 'select', 'argument_name': 'kernel', 'argument_items': ['GaussianKernel', 'PolynomialKernel', 'LinearKernel'], 'argument_default': 'GaussianKernel', 'argument_explain': 'Your choice for the covariance function' }, { 'argument_type': 'integer', 'argument_name': 'degree', 'argument_default': '5', 'argument_explain': 'The degree to use with the PolynomialKernel' }, { 'argument_type': 'decimal', 'argument_label': 'Kernel Width', 'argument_name': 'sigma', 'argument_default': '2.0', 'argument_explain': 'The sigma to use in the GaussianKernel' }, { 'argument_type': 'decimal', 'argument_label': 'Noise Level', 'argument_name': 'noise_level', 'argument_default': '0.1', 'argument_explain': 'The noise level of the training points' }, { 'argument_type': 'button-group', 'argument_items': [{'button_name': 'TrainGP', 'button_type': 'json_up_down_load'}, {'button_name': 'UseML2', 'button_type': 'json_up_down_load'}, {'button_name': 'clear'}] } ] properties = { 'title': 'Gaussian Process Regression', 'template': {'type': 'coordinate-2dims', 'mouse_click_enabled': 'left', 'coordinate_system': {'horizontal_axis': {'range': [-5, 5]}, 'vertical_axis': {'range': [-5, 5]}}, 'description': read_demo_description.read_description(__file__)}, 'panels': [ { 'panel_name': 'arguments', 'panel_label': 'Arguments', 'panel_property': arguments }, { 'panel_name': 'toy_data', 'panel_label': 'Toy Data' }]} return render_to_response("regression/gaussian_process.html", properties, context_instance = RequestContext(request))
def entrance(request): properties = { 'title' : 'Digit Recognize', 'template': {'type': 'drawing', 'description': read_demo_description.read_description(__file__)}, 'panels': [ { 'panel_name': 'preview', 'panel_label': 'Preview'}]} return render_to_response("application/ocr.html", properties, context_instance = RequestContext(request))
def entrance(request): arguments = [ { "argument_type": "select", "argument_name": "kernel", "argument_items": ["GaussianKernel", "PolynomialKernel", "LinearKernel"], "argument_default": "GaussianKernel", "argument_explain": "Your choice for the covariance function", }, { "argument_type": "integer", "argument_name": "degree", "argument_default": "5", "argument_explain": "The degree to use with the PolynomialKernel", }, { "argument_type": "decimal", "argument_label": "Kernel Width", "argument_name": "sigma", "argument_default": "2.0", "argument_explain": "The sigma to use in the GaussianKernel", }, { "argument_type": "decimal", "argument_label": "Noise Level", "argument_name": "noise_level", "argument_default": "0.1", "argument_explain": "The noise level of the training points", }, { "argument_type": "button-group", "argument_items": [ {"button_name": "TrainGP", "button_type": "json_up_down_load"}, {"button_name": "clear"}, ], }, ] properties = { "title": "Gaussian Process Regression", "template": { "type": "coordinate-2dims", "mouse_click_enabled": "left", "coordinate_system": {"horizontal_axis": {"range": [-5, 5]}, "vertical_axis": {"range": [-5, 5]}}, "description": read_demo_description.read_description(__file__), }, "panels": [ {"panel_name": "arguments", "panel_label": "Arguments", "panel_property": arguments}, {"panel_name": "toy_data", "panel_label": "Toy Data"}, ], } return render_to_response("regression/gaussian_process.html", properties, context_instance=RequestContext(request))
def entrance(request): properties = { 'title': 'Digit Recognize', 'template': { 'type': 'drawing', 'description': read_demo_description.read_description(__file__) }, 'panels': [{ 'panel_name': 'preview', 'panel_label': 'Preview' }] } return render_to_response("application/ocr.html", properties, context_instance=RequestContext(request))
def entrance(request): arguments = [ { 'argument_type': 'select', 'argument_label': 'Kernel Function', 'argument_name': 'kernel', 'argument_items': ['GaussianKernel', 'PolynomialKernel', 'LinearKernel'], 'argument_default': 'GaussianKernel', 'argument_explain': 'Kernel Function'}, { 'argument_type': 'decimal', 'argument_label': 'Kernel Width', 'argument_name': 'kernel_width', 'argument_default': '0.3', 'argument_explain': 'The sigma to use in the GaussianKernel'}, { 'argument_type': 'integer', 'argument_name': 'degree', 'argument_default': '5', 'argument_explain': 'The degree of the PolynomialKernel'}, { 'argument_type': 'button-group', 'argument_items': [{'button_name': 'generate', 'button_type': 'json_up_down_load'}, {'button_name': 'clear'}]}, ] properties = { 'title': 'Kernel Matrix Visualization', 'template': {'type': 'coordinate-2dims', 'mouse_click_enabled': 'left', 'heatmap': { 'contour': True }, 'coordinate_system': {'horizontal_axis': {'range':[-5.0, 5.0]}, 'vertical_axis': {'range':[-4.0, 4.0]}}, 'description': read_demo_description.read_description(__file__)}, 'panels': [ { 'panel_name': 'arguments', 'panel_label': 'Arguments', 'panel_property': arguments }, { 'panel_name': 'toy_data', 'panel_label': 'Toy Data' }]} return render_to_response("misc/kernel_matrix.html", properties, context_instance = RequestContext(request))
def entrance(request): arguments = [ { 'argument_type': 'select', 'argument_name': 'demo_switch', 'argument_label': 'Feature Type', 'argument_items': ['MIT_CBCL_faces_embedding', 'words_embedding', 'MNIST_digits_embedding', 'promoters_embedding', 'faces_embedding'], }, { 'argument_type': 'decimal', 'argument_name': 'k', 'argument_label': 'k', 'argument_default': '20', 'argument_explain': 'Number of neighbors to consider' }, { 'argument_type': 'button-group', 'argument_items': [{'button_name': 'show'}, {'button_name': 'clear'}], } ] properties = {'title': 'Dimension Reduction', 'template': {'type': 'coordinate-2dims', 'mouse_click_enabled': 'none', 'description': read_demo_description.read_description(__file__)}, 'panels':[ { 'panel_name': 'arguments', 'panel_label': 'Arguments', 'panel_property': arguments }]} return render_to_response("dimred/tapkee.html", properties, context_instance = RequestContext(request))
'x_range': [0, 1], 'y_range': [0, 1] }] properties = { 'title': 'KMeans', 'template': {'type': 'coordinate-2dims', 'feature': 'binary', 'coordinate_range': {'horizontal': [0, 1], 'vertical': [0, 0.8]}, 'coordinate_system': {'horizontal_axis': {'position': 'bottom', 'label': 'X-axis', 'range': [0, 1]}, 'vertical_axis': {'position': 'left', 'label': 'Y-axis', 'range': [0, 1]}}, 'description': read_demo_description.read_description(__file__), 'mouse_click_enabled': 'both'}, 'panels': [ { 'panel_name': 'arguments', 'panel_label': 'Arguments', 'panel_property': arguments }, { 'panel_name': 'toy_data', 'panel_label': 'Toy Data', 'panel_property': toy_data_arguments}], 'data_sets' : ['diabetes']} def entrance(request): return render_to_response("clustering/kmeans.html", properties, context_instance=RequestContext(request))
def entrance(request): arguments = [ { 'argument_type': 'select', 'argument_name': 'kernel', 'argument_items': ['GaussianKernel', 'PolynomialKernel', 'LinearKernel' ], 'argument_default': 'GaussianKernel', 'argument_explain': '<i>Kernel</i> Function', }, { 'argument_type': 'decimal', 'argument_name': 'C', 'argument_default': '1.2', 'argument_explain': 'Penalty parameter of the error term' }, { 'argument_type': 'decimal', 'argument_name': 'tube', 'argument_default': '0.04', 'argument_explain': 'Specifies the allowed deviation of the prediction from the actual value' }, { 'argument_type': 'decimal', 'argument_name': 'sigma', 'argument_default': '0.3', 'argument_explain': 'The sigma to use in the GaussianKernel' }, { 'argument_type': 'integer', 'argument_name': 'degree', 'argument_default': '5', 'argument_explain': 'The degree to use in the PolynomialKernel' }, { 'argument_type': 'button-group', 'argument_items': [{'button_name': 'regress', 'button_type': 'json_up_down_load'}, {'button_name': 'clear'}] } ] toy_data_arguments = [ { 'problem_type': 'regression', } ] properties = { 'title': 'Supported Vector Regression', 'template': {'type': 'coordinate-2dims', 'heatmap': False, 'coordinate_system': {'horizontal_axis': {'position': 'bottom', 'label': 'x-axis', 'range': [0, 1]}, 'vertical_axis': {'position': 'left', 'label': 'y-axis', 'range': [0, 1]}}, 'mouse_click_enabled': 'left', 'description': read_demo_description.read_description(__file__)}, 'panels': [ { 'panel_name': 'arguments', 'panel_label': 'Arguments', 'panel_property': arguments }, { 'panel_name': 'toy_data', 'panel_label': 'toy data', 'panel_property': toy_data_arguments }]} return render_to_response("regression/support_vector_regression.html", properties, context_instance=RequestContext(request))
def entrance(request): arguments = [{ 'argument_type': 'select', 'argument_label': 'Regression', 'argument_name': 'regression', 'argument_items': [ 'LeastSquaresRegression', 'LinearRidgeRegression', 'KernelRidgeRegression' ], 'argument_explain': 'Regression tool' }, { 'argument_type': 'decimal', 'argument_name': 'sigma', 'argument_default': '0.3', 'argument_explain': 'For GaussianKernel (KernelRidgeRegression)' }, { 'argument_type': 'decimal', 'argument_name': 'Tau', 'argument_default': '5', 'argument_explain': 'tau to use in the (Kernel)RidgeRegression' }, { 'argument_type': 'button-group', 'argument_items': [{ 'button_name': 'regress', 'button_type': 'json_up_down_load' }, { 'button_name': 'clear' }] }] toy_data_arguments = [{ 'problem_type': 'regression', }] properties = { 'title': 'Regression', 'template': { 'type': 'coordinate-2dims', 'heatmap': False, 'coordinate_system': { 'horizontal_axis': { 'position': 'bottom', 'label': 'x-axis', 'range': [0, 1] }, 'vertical_axis': { 'position': 'left', 'label': 'y-axis', 'range': [0, 1] } }, 'mouse_click_enabled': 'left', 'description': read_demo_description.read_description(__file__) }, 'panels': [{ 'panel_name': 'arguments', 'panel_label': 'Arguments', 'panel_property': arguments }, { 'panel_name': 'toy_data', 'panel_label': 'toy data', 'panel_property': toy_data_arguments }], 'data_sets': ['boston_housing'] } return render_to_response("regression/regression.html", properties, context_instance=RequestContext(request))
def entrance(request): arguments = [{ 'argument_type': 'select', 'argument_name': 'kernel', 'argument_items': ['GaussianKernel', 'PolynomialKernel', 'LinearKernel'], 'argument_default': 'GaussianKernel', 'argument_explain': 'Your choice for the covariance function' }, { 'argument_type': 'select', 'argument_label': 'Inference Method', 'argument_name': 'inf', 'argument_items': ['ExactInferenceMethod', 'FITCInferenceMethod'], 'argument_default': 'ExactInferenceMethod', 'argument_explain': 'Your choice for the Inference method' }, { 'argument_type': 'integer', 'argument_name': 'degree', 'argument_default': '5', 'argument_explain': 'The degree to use with the PolynomialKernel' }, { 'argument_type': 'decimal', 'argument_label': 'Kernel Width', 'argument_name': 'sigma', 'argument_default': '1.0', 'argument_explain': 'The sigma to use in the GaussianKernel' }, { 'argument_type': 'decimal', 'argument_label': 'Noise Level', 'argument_name': 'noise_level', 'argument_default': '0.1', 'argument_explain': 'The noise level of the training points' }, { 'argument_type': 'decimal', 'argument_name': 'scale', 'argument_label': 'Kernel scaling', 'argument_default': '1.0', 'argument_explain': 'The scale for kernel' }, { 'argument_type': 'select', 'argument_label': 'Learn parameters', 'argument_name': 'learn', 'argument_items': ['No', 'ML2'], 'argument_explain': 'Learn parameters using model selection' }, { 'argument_type': 'button-group', 'argument_items': [{ 'button_name': 'TrainGP', 'button_type': 'json_up_down_load' }, { 'button_name': 'plot_predictive', 'button_type': 'json_up_down_load' }, { 'button_name': 'clear' }] }] toy_data_arguments = [{ 'problem_type': 'regression', }] properties = { 'title': 'Gaussian Process Regression', 'template': { 'type': 'coordinate-2dims', 'mouse_click_enabled': 'both', 'coordinate_system': { 'horizontal_axis': { 'range': [-5, 5] }, 'vertical_axis': { 'range': [-5, 5] } }, 'heatmap': { 'contour': True }, 'description': read_demo_description.read_description(__file__) }, 'panels': [{ 'panel_name': 'arguments', 'panel_label': 'Arguments', 'panel_property': arguments }, { 'panel_name': 'toy_data', 'panel_label': 'Toy Data', 'panel_property': toy_data_arguments }] } return render_to_response("regression/gaussian_process.html", properties, context_instance=RequestContext(request))
def description_by_url(url): return read_demo_description.read_description(DEMOS_PATH + url[:-1] + ".py")
def entrance(request): arguments = [ { 'argument_type': 'select', 'argument_label': 'Regression', 'argument_name': 'regression', 'argument_items': ['LeastSquaresRegression', 'LinearRidgeRegression', 'KernelRidgeRegression'], 'argument_explain': 'Regression tool' }, { 'argument_type': 'decimal', 'argument_name': 'sigma', 'argument_default': '0.3', 'argument_explain': 'For GaussianKernel (KernelRidgeRegression)' }, { 'argument_type': 'decimal', 'argument_name': 'Tau', 'argument_default': '5', 'argument_explain': 'tau to use in the (Kernel)RidgeRegression' }, { 'argument_type': 'button-group', 'argument_items': [{'button_name': 'regress', 'button_type': 'json_up_down_load'}, {'button_name': 'clear'}] }] toy_data_arguments = [ { 'problem_type': 'regression', } ] properties = { 'title': 'Regression', 'template': {'type': 'coordinate-2dims', 'heatmap': False, 'coordinate_system': {'horizontal_axis': {'position': 'bottom', 'label': 'x-axis', 'range': [0, 1]}, 'vertical_axis': {'position': 'left', 'label': 'y-axis', 'range': [0, 1]}}, 'mouse_click_enabled': 'left', 'description': read_demo_description.read_description(__file__)}, 'panels': [ { 'panel_name': 'arguments', 'panel_label': 'Arguments', 'panel_property': arguments }, { 'panel_name': 'toy_data', 'panel_label': 'toy data', 'panel_property': toy_data_arguments }], 'data_sets' : ['boston_housing'] } return render_to_response("regression/regression.html", properties, context_instance=RequestContext(request))