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public BikeSharingDemandPrediction Predict(BikeSharingDemandSample sample)
{
    return _predictionEngine.Predict(sample);
}

public RegressionMetrics Evaluate(string testDataLocation)
{
    var testDataView = _mlContext.Data.LoadFromTextFile<BikeSharingDemandSample>(
                                path: testDataLocation,
                                hasHeader: true,

NMZivkovic

public Model(MLContext mlContext, IEstimator<ITransformer> algorythm, string trainingDataLocation)
{
    _mlContext = mlContext;
    _algorythim = algorythm;
    _trainingDataView = _mlContext.Data.LoadFromTextFile<BikeSharingDemandSample>(
                                path: trainingDataLocation,
                                hasHeader: true,
                                separatorChar: ',');

    Name = algorythm.GetType().ToString().Split('.').Last();

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public void BuildAndFit()
{
    var pipeline = _mlContext.Transforms.CopyColumns(inputColumnName: "Count", outputColumnName: "Label")
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Season"))
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Year"))
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Holiday"))
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Weather"))
        .Append(_mlContext.Transforms.Concatenate("Features",
                                        "Season",
                                        "Year",

NMZivkovic

using BikeSharingDemand.BikeSharingDemandData;
using Microsoft.ML;
using Microsoft.ML.Data;
using System.Linq;

namespace BikeSharingDemand.ModelNamespace
{
    public sealed class Model
    {
        private readonly MLContext _mlContext;

NMZivkovic

using Microsoft.ML.Data;

namespace BikeSharingDemand.BikeSharingDemandData
{
    public class BikeSharingDemandSample
    {
        [LoadColumn(2)] public float Season;
        [LoadColumn(3)] public float Year;
        [LoadColumn(4)] public float Month;
        [LoadColumn(5)] public float Hour;

NMZivkovic

for e in range(0, num_of_episodes):
    # Reset the enviroment
    state = enviroment.reset()
    state = np.reshape(state, [1, 1])
    
    # Initialize variables
    reward = 0
    terminated = False
    
    bar = progressbar.ProgressBar(maxval=timesteps_per_episode/10, widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])

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optimizer = Adam(learning_rate=0.01)
agent = Agent(enviroment, optimizer)

batch_size = 32
num_of_episodes = 100
timesteps_per_episode = 1000
agent.q_network.summary()

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def retrain(self, batch_size):
    minibatch = random.sample(self.expirience_replay, batch_size)

    for state, action, reward, next_state, terminated in minibatch:

        target = self.q_network.predict(state)

        if terminated:
            target[0][action] = reward
        else:

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def act(self, state):
    if np.random.rand() <= self.epsilon:
        return enviroment.action_space.sample()

    q_values = self.q_network.predict(state)
    return np.argmax(q_values[0])

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def _build_compile_model(self):
    model = Sequential()
    model.add(Embedding(self._state_size, 10, input_length=1))
    model.add(Reshape((10,)))
    model.add(Dense(50, activation='relu'))
    model.add(Dense(50, activation='relu'))
    model.add(Dense(self._action_size, activation='linear'))

    model.compile(loss='mse', optimizer=self._optimizer)
    return model