philippslang

if 1:
    model = make_rnn(num_features, num_targets, num_timesteps, num_units)
    model.fit(features, targets, epochs=10, batch_size=24, validation_split=0.2)
    model.save(FNAME_MODEL)
else:
    model = kem.load_model(FNAME_MODEL)

philippslang

time_stage_change = 15.
num_production_history = 8 # number of productin history points used for prediction
time, production, stage = calc_two_stage_decline(p0, exp_stage_zero, exp_stage_one, time_max, 
  time_stage_change, num=num_timesteps)    

features = np.full((1, num_timesteps, num_features), NA)
features[0, :num_production_history, ifeature_production] = production[:num_production_history]
features[0, :, ifeature_stage] = stage[:]
normalizer_features.transform(features.reshape(num_timesteps, num_features))
targets = model.predict(features) 

philippslang

ghci> (0.3+0.2)+0.1
0.6
ghci> 0.3+(0.2+0.1)
0.6000000000000001

philippslang

{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Python: loadbalancer test",
            "type": "python",
            "request": "launch",
            "module": "pytest",
            "args": [
                "--no-cov"

philippslang

var request = require('request');

module.exports = function(context, perf) {
    request('http://ixciperf.westeurope.cloudapp.azure.com:5000/api/update', function (error, response, body) {
        if (error) {
            context.log(error);
        }

        if (!error && response.statusCode == 200) {
           context.log("JavaScript blob trigger function processed blob \n Name:", context.bindingData.name, "\n Blob Size:", perf.length, "Bytes");

philippslang

{
    "search.exclude": {
        "**/node_modules": true,
        "**/bower_components": true,
        "**/venv/**": true,
        "**/.git/**": true,
        },
    "files.watcherExclude": {
        "**/.git/**": true,
        "**/venv/**": true