What's New in ODYSSEE Solver 2024.2

ODYSSEE Solver includes:

• ODYSSEE QUASAR: The solver to make predictive models and data mining analysis.
• ODYSSEE NOVA: The optimizer to search for optimal variables to minimize an objective function, by respecting constraints.

With ODYSSEE Solver, you can:

• Make a scientific analyze with the datamining.
• Process matrices, files, and images
• Constitute predictive models.
• Make scripting.

What's New in ODYSSEE Solver 2024.2 Patch

ODYSSEE Nova– Optimizer

• Bug Fixes
• • For the NSGAII optimizer,
    • When the number of variables was greater than the number of objectives, the calculation was not completed. This is fixed.
    • When an inequality constraint (H<val) was defined, the pareto front displayed points respecting the opposite of the defined constraint (H>val). This is fixed.

What's New in ODYSSEE Solver 2024.2

ODYSSEE Quasar – Solver

• New Statistic functions implemented:
  • kde: kernel density estimation (KDE) function corresponds to the kernel smoothing of a finite data sample (matrix X), which output a continuous probability density estimation at each output point. The most common kernel is a Gaussian function. The last argument nbrOutputPoints determines number of equidistant points to compute KDE at.

ext("statistics", "kde", X, nbrOutputPoints)

• • Quantile function gives sample quantiles: zeroth quantile or minimum value (Q0), Quantile (Q1), 2nd quantile or median (Q2), Quantile (Q3), 4th quantile or maximum value (Q4) computed for each column of input matrix X.

ext("statistics", "quantile", X, computeMethod)

• • Histogram function has been documented: This function displays the frequencies of data included in the different bins defined by the user with the variable nbr_bins.

ext("operator","matrix","histogram", X, nbr_bins)

• • effects_withstats function provides more information for a linear regression than just the values of coefficients, based on some assumptions for a linear regression:

• • standard deviation error associated to the coefficient computed.
  • Confident interval for the coefficient, based on provided confidence level alpha.
  • p-value that shows the significance of a variable. p-value determines if obtained effects pf a variable, could be simply due to randomness or chance.
  • sensitivity index (SI) which shows how much the output can change (relatively) by changing independently each input variable. it is normalized based on range of Y.
  • Confidence and Prediction intervals for the linear model.

ext("mining","effects_withstats",X,Y, norm, order, "coeff_output", alpha)

• Modified Unet function has been added. It is an images processing algorithm using a semantic segmentation process that consists of labeling each pixel of an input PNG image with a class. This function based on neural network is using for the learning part the vgg pretrained model and a defined user database of PNG and semantic images (mask). Learning dataset size can be relatively small: starting from tens. The number of classes in the images must be given by the user.

learnModifiedUnet(lr, batchsize, epochs, resize_width, resize_height, num_classes, validationPercent, dataset_path_img, dataset_path_mask, output_model_name)

predictModifiedUnet(New_image_file, resize_width, resize_height, num_classes, model_name, save_file, DisplayOutput)

• VAR (Vector Auto Regression) function is an additional method used in time series analysis to model the dynamic relationship between multiple time series variables: Y(t) = function(Y, X1, X2,.., from time t-1 to t-n).

VAR(X, Y, X_toTest, Yinit, regression_order, difference_type, integrator)

• Bug Fixes
  • The regression function used to compute the regression coefficient on XY database has been updated to handle the correlated predictors (columns of X input matrix).

We are now using the following simpler function:

COEFFS = ext("mining", "effectsqr", X, Y, "parameter_effect_")

instead of

COEFFS = ext("mining", "effects", X, Y, 0, ORDER, "parameter_effect_")

ODYSSEE Nova – Optimizer

• NSGAII (Non-dominated Sorting Genetic Algorithm II), is a new global multi-objective evolutionary algorithm, method #19, looking for the best candidates to plot an optimal Front Pareto. To reach that, the points are ranked in level where the front rank contains candidates with a pareto distance is minimum, with no violating constraints.  So, as iterations go on, the algorithm seeks to obtain a single rank with a minimum crowding distance between candidates to plot the optimal Front Pareto.
• Bugs fixed:
  • For NLPQLP and SLSQP, nova was able stay lock at the end of the iteration loops without giving up control.
  • For the DNN function executed under Linux, the function was launched but remained frozen before the “epoch” message. It is fixed.
  • A French message was displayed if the bad path of Nova.exe was used. It is now translated.