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_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 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))
