def POST(self): expType = web.input()["type"] name = web.input()["name"] cerebro = CerebroModel.get() cerebro.name = name results = cerebro.runCurrentExperiment(expType, True) return json.dumps(results)
def POST(self): """ Get information about the current model at a specific timestep""" cerebro = CerebroModel.get() dataInput = dict(web.input()) data = cerebro.getDataAtTime(dataInput) web.header("Content-Type", "application/json") return json.dumps(data)
def POST(self): """ Create a dataset from a function """ cerebro = CerebroModel.get() fnText = web.input()["text"] iterations = int(web.input()["iterations"]) cerebro.createProceduralDataset(fnText, iterations) modelDesc = cerebro.getCurrentModelParams() return pprint.pformat(modelDesc['modelParams'])
def POST(self): """ Create a dataset from a function """ cerebro = CerebroModel.get() fnText = web.input()["text"] iterations = int(web.input()["iterations"]) cerebro.createProceduralDataset(fnText, iterations) modelDesc = cerebro.getCurrentModelParams() return pprint.pformat(modelDesc["modelParams"])
def GET(self): """ FIXME: Right now, this returns the csv as a text file, so that the user can use the "save file as" button """ cerebro = CerebroModel.get() text = cerebro.getDescriptionText() web.header("Content-Type", "text/plain") web.header("Content-Disposition", "attachment; filename=description.py") return text
def GET(self): """ FIXME: Right now, this returns the csv as a text file, so that the user can use the "save file as" button """ cerebro = CerebroModel.get() text = cerebro.getDatasetText() web.header("Content-Type", "text/plain") web.header('Content-Disposition', "attachment; filename=data.csv") return text
def POST(self): """ Load a dataset/model from a description.py """ cerebro = CerebroModel.get() params = web.input() cerebro.loadDescriptionFile( descriptionFile=params["experimentFile"], subDescriptionFile=params["subExperimentFile"]) modelDesc = cerebro.getCurrentModelParams() return pprint.pformat(modelDesc['modelParams'])
def POST(self): """ Load a dataset/model from a description.py """ cerebro = CerebroModel.get() params = web.input() cerebro.loadDescriptionFile( descriptionFile=params["experimentFile"], subDescriptionFile=params["subExperimentFile"] ) modelDesc = cerebro.getCurrentModelParams() return pprint.pformat(modelDesc["modelParams"])
def POST(self): cerebro = CerebroModel.get() returnData = json.dumps(cerebro.getLatestPredictions()) web.header("Content-Type", "application/json") return returnData
def POST(self): cerebro = CerebroModel.get() cerebro.stopCurrentExperiment() return ""
def POST(self): newThreshold = web.input()["threshold"] cerebro = CerebroModel.get() results = cerebro.setClassifierThreshold(newThreshold) return json.dumps(results)
def POST(self): cerebro = CerebroModel.get() params = eval(web.input()['params']) cerebro.setModelParams(params) return
def POST(self): cerebro = CerebroModel.get() params = eval(web.input()["params"]) cerebro.setModelParams(params) return
def POST(self): """ Save the currently used mongoDB for use later """ name = web.input()["name"] cerebro = CerebroModel.get() results = cerebro.setExperimentName(name) return json.dumps(results)
def POST(self): cerebro = CerebroModel.get() predictedFieldname = web.input()['fieldname'] cerebro.setPredictedField(fieldname=predictedFieldname) return ""
def POST(self): cerebro = CerebroModel.get() predictedFieldname = web.input()["fieldname"] cerebro.setPredictedField(fieldname=predictedFieldname) return ""