Clone the repository from GitHub:
git clone https://github.com/naka-tomo/serket.git
Install dependency packages (you can skip this step if you have already installed these packages):
pip install numpy pip install opencv-python pip install numba pip install scipy pip install tensorflow pip install librosa==0.5.1
First, the toy dataset of six observations is generated. Here, these are assumed to be generated from three categories of latent variables.
data = [ [10, 2, 1], [8, 1, 1], [2, 7, 0], [1, 11, 2], [1, 1, 8], [2, 1, 1] ] data_category = [0, 0, 1, 1, 2, 2]
Next, we define the modules.
srk.Observation is a module that sends observations to another module and
mlda.MLDA (multimodal latent Dirichlet allocation) is a module for unsupervised classification.
Here, we define a kind of MLDA that classifies the dataset into three classes.
By using the optional argument
category, the classification accuracy can be computed automatically.
obs = srk.Observation( data ) mlda1 = mlda.MLDA( 3 , category=data_category )
By connecting modules, the model is constructed and the parameters of the model are estimated by the
mlda1.connect( obs ) mlda1.update()
If the model was successfully trained, you can find the
module000_mlda directory in your working directory, which contains the training results.
The classification accuracy that is computed automatically is saved in
It might indicate a value of 1.00, which represents the all observations were classified correctly.