{"action":"closed","issue":{"url":"https://api.github.com/repos/numenta/nupic/issues/2680","labels_url":"https://api.github.com/repos/numenta/nupic/issues/2680/labels{/name}","comments_url":"https://api.github.com/repos/numenta/nupic/issues/2680/comments","events_url":"https://api.github.com/repos/numenta/nupic/issues/2680/events","html_url":"https://github.com/numenta/nupic/issues/2680","id":111878954,"number":2680,"title":"Always same energy_consumption and predicted value","user":{"login":"enricgui","id":14308809,"avatar_url":"https://avatars.githubusercontent.com/u/14308809?v=3","gravatar_id":"","url":"https://api.github.com/users/enricgui","html_url":"https://github.com/enricgui","followers_url":"https://api.github.com/users/enricgui/followers","following_url":"https://api.github.com/users/enricgui/following{/other_user}","gists_url":"https://api.github.com/users/enricgui/gists{/gist_id}","starred_url":"https://api.github.com/users/enricgui/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/enricgui/subscriptions","organizations_url":"https://api.github.com/users/enricgui/orgs","repos_url":"https://api.github.com/users/enricgui/repos","events_url":"https://api.github.com/users/enricgui/events{/privacy}","received_events_url":"https://api.github.com/users/enricgui/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/numenta/nupic/labels/question","name":"question","color":"cc317c"}],"state":"closed","locked":false,"assignee":{"login":"rhyolight","id":15566,"avatar_url":"https://avatars.githubusercontent.com/u/15566?v=3","gravatar_id":"","url":"https://api.github.com/users/rhyolight","html_url":"https://github.com/rhyolight","followers_url":"https://api.github.com/users/rhyolight/followers","following_url":"https://api.github.com/users/rhyolight/following{/other_user}","gists_url":"https://api.github.com/users/rhyolight/gists{/gist_id}","starred_url":"https://api.github.com/users/rhyolight/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/rhyolight/subscriptions","organizations_url":"https://api.github.com/users/rhyolight/orgs","repos_url":"https://api.github.com/users/rhyolight/repos","events_url":"https://api.github.com/users/rhyolight/events{/privacy}","received_events_url":"https://api.github.com/users/rhyolight/received_events","type":"User","site_admin":false},"milestone":null,"comments":9,"created_at":"2015-10-16T17:50:00Z","updated_at":"2015-10-17T16:26:20Z","closed_at":"2015-10-17T16:26:20Z","body":"I am developing an application which is using at this stage same model_params and input data as hot_gym tutorial, but working from a database source instead of files.\r\n\r\nThe problem is that I am always getting the same value for prediction as passed as consumption energy.\r\n\r\nI already checked out that I am providing proper datetime and float values. Model is created, saved and loaded again apparently properly since its running with no errors.\r\n\r\nAny idea for this newbie?\r\n\r\nThanks in advance.\r\n\r\nNOTE: example of output [consumption, prediction, anomalyScore]:\r\n```\r\n[21.2, 21.2, None]\r\n[16.4, 16.4, None]\r\n[4.7, 4.7, None] ----> this one is supposed to predict 16.4 in pure hot_gym tutorial\r\n[4.7, 4.7, None]\r\n[4.6, 4.6, None]\r\n[23.5, 23.5, None]\r\n[47.5, 47.5, None]\r\n[45.4, 45.4, None]\r\n[46.1, 46.1, None]\r\n[41.5, 41.5, None]\r\n```\r\n\r\nNOTE: here is partial code\r\n```python\r\n        metricsManager = MetricsManager(_METRIC_SPECS, model.getFieldInfo(),\r\n                                  model.getInferenceType())\r\n        stringdate = \"%s\" % row[\"time\"]\r\n        timestamp = datetime.datetime.strptime(stringdate, \"%Y-%m-%d %H:%M:%S\")\r\n        consumption = float(row[\"value\"])\r\n        result = model.run({\r\n            \"timestamp\": timestamp,\r\n            \"kw_energy_consumption\": consumption\r\n        })\r\n        result.metrics = metricsManager.update(result)\r\n        prediction = result.inferences[\"multiStepBestPredictions\"][1]\r\n        anomalyScore = result.inferences[\"anomalyScore\"]\r\n```"}}