FileTransfer | Transfer any files from PC to your phone | File Utils library

An histogram of a sample can approximate the pdf of the distribution under the assumption that the sample is enough to capture the distribution of the population. So when you use histogram_train = rv_histogram(np.histogram(data_train_histogram, bins='auto')) it generates a distribution given by a histogram. It has a .pdf method to evaluate the pdf and also .rvs to generate values that follow this distribution. So to calculate the Kullback–Leibler divergence between two distributions you can do the following:

Use dayfirst=True as parameter of read_csv:

Look at https://capacitorjs.com/cordova

also https://ionicframework.com/docs/intro/upgrading-to-ionic-6

Your old plugin supports only cordova, you need to remove an old plugin and install a new plugin one by one.

When I change

The standard approach is to use an encoder-decoder architecture (see 1 and 2 for instance):

• The encoder takes as input the past values of the features and of the target and returns an output representation.
• The decoder takes as input the encoder output and the future values of the features and returns the predicted values of the target.

You can use any architecture for the encoder and for the decoder and you can also consider different approaches for passing the encoder output to the decoder (e.g. adding or concatenating it to the decoder input features, adding or concatenating it to the output of some intermediate decoder layer, or adding it to the final decoder output), the code below is just an example.

You don't have any element with user_para assigned as its Id. Maybe you want

It may be useful to step through the model inputs/outputs in detail.

When using the keras.layers.SimpleRNN layer with return_sequences=True, the output will return a 3-D tensor where the 0th axis is the batch size, the 1st axis is the timestep, and the 2nd axis is the number of hidden units (in the case for both SimpleRNN layers in your model, 10).

The Conv1D layer will produce an output tensor where the last dimension becomes the number of hidden units (in the case for your model, 16), as it's just being convolved with the input.

keras.layers.TimeDistributed, the layer supplied (in the example provided, Dense(1)) will be applied to each timestep in the batch independently. So with 96 timesteps, we have 96 outputs for each record in the batch.

So stepping through your model:

First, I suggest you read Tensorflow's tutorial on time series forecasting. I played around a bit with your code and the data provided. The first important thing is that only the temperature column contains information. In the code below, I prepare the data so that X over a time window of 96 samples/steps and the next step is in Y. X is of dimension (n_samples, 96, 1) and Y of dimension (n_samples, ), I use only steps_backwards points for the past (and discarded the future for simplicity, without affecting the generality) I have tried different models (a simple Fully Connected or RNN + FC, etc.). I'm doing mean pooling (with the functional API rather than the sequential model definition approach) so that I have a single predicted value at the end.

Two mistakes here.

You are using the .\ operator, which represents the current directory. You then attempt to use the environment variable %systemroot%, which would resolve to something like: