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'numpy.ndarray' object has no attribute 'index' #293

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GaidaiIgor opened this issue Jul 4, 2024 · 1 comment
Open

'numpy.ndarray' object has no attribute 'index' #293

GaidaiIgor opened this issue Jul 4, 2024 · 1 comment
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@GaidaiIgor
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GaidaiIgor commented Jul 4, 2024

On line 1683 in pygad.py the code says
solution_idx = self.best_solutions.index(list(sol))
apparently assuming that self.best_solutions is always a list. However, it is possible that self.best_solutions is a numpy.ndarray, in which case the index method is undefined and the program crashes.

Adding

if type(self.best_solutions) is numpy.ndarray:
    self.best_solutions = self.best_solutions.tolist()

before the first if statement (if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions):) seems to resolve the issue.

@ahmedfgad ahmedfgad added the bug Something isn't working label Jan 7, 2025
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Thanks. This is going to be fixed in the next release!

ahmedfgad added a commit that referenced this issue Jan 7, 2025
1. The `delay_after_gen` parameter is removed from the `pygad.GA` class constructor. As a result, it is no longer an attribute of the `pygad.GA` class instances. To add a delay after each generation, apply it inside the `on_generation` callback. #283
2. In the `single_point_crossover()` method of the `pygad.utils.crossover.Crossover` class, all the random crossover points are returned before the `for` loop. This is by calling the `numpy.random.randint()` function only once before the loop to generate all the K points (where K is the offspring size). This is compared to calling the `numpy.random.randint()` function inside the `for` loop K times, once for each individual offspring.
3. Bug fix in the `examples/example_custom_operators.py` script. #285
4. While making prediction using the `pygad.torchga.predict()` function, no gradients are calculated.
5. The `gene_type` parameter of the `pygad.helper.unique.Unique.unique_int_gene_from_range()` method accepts the type of the current gene only instead of the full gene_type list.
6. Created a new method called `unique_float_gene_from_range()` inside the `pygad.helper.unique.Unique` class to find a unique floating-point number from a range.
7. Fix a bug in the `pygad.helper.unique.Unique.unique_gene_by_space()` method to return the numeric value only instead of a NumPy array.
8. Refactoring the `pygad/helper/unique.py` script to remove duplicate codes and reformatting the docstrings.
9. The plot_pareto_front_curve() method added to the pygad.visualize.plot.Plot class to visualize the Pareto front for multi-objective problems. It only supports 2 objectives. #279
10. Fix a bug converting a nested NumPy array to a nested list. #300
11. The `Matplotlib` library is only imported when a method inside the `pygad/visualize/plot.py` script is used. This is more efficient than using `import matplotlib.pyplot` at the module level as this causes it to be imported when `pygad` is imported even when it is not needed. #292
12. Fix a bug when minus sign (-) is used inside the `stop_criteria` parameter (e.g. `stop_criteria=["saturate_10", "reach_-0.5"]`). #296
13. Make sure `self.best_solutions` is a list of lists inside the `cal_pop_fitness` method. #293
14. Fix a bug where the `cal_pop_fitness()` method was using the `previous_generation_fitness` attribute to return the parents fitness. This instance attribute was not using the fitness of the latest population, instead the fitness of the population before the last one. The issue is solved by updating the `previous_generation_fitness` attribute to the latest population fitness before the GA completes. #291
@ahmedfgad ahmedfgad mentioned this issue Jan 7, 2025
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