def __make_data_matrix(all_genes, all_fitnesses): par_dtype = [(par, int) for par in parameters_in_genes] fit_dtype = [(stat, float) for stat in fitness_types.keys()] par_matrix = np.zeros(shape = len(all_genes), dtype = par_dtype) fit_matrix = np.zeros(shape = len(all_genes), dtype = fit_dtype) for i, (gene, fitness) in enumerate(zip(all_genes, all_fitnesses)): for par, val in gene.items(): par_matrix[par][i] = val for k, stat in enumerate(fitness_weights.keys()): fit_matrix[stat][i] = fitness[k] return DataFrame(par_matrix), DataFrame(fit_matrix)
def store_generation_as_data_matrix(generation_data, generation_number, path):
all_genes = list()
all_fitnesses = list()
all_ids = list()
for gene in generation_data:
all_genes.append(gene['ind'])
all_fitnesses.append(gene['fit'])
if gene.has_key('data_ids'):
all_ids.append(gene['data_ids'])
par_dtype = [(par, int) for par in parameters_in_genes]
fit_dtype = [(stat, float) for stat in fitness_types.keys()]
par_matrix = np.zeros(shape = len(all_genes), dtype = par_dtype)
fit_matrix = np.zeros(shape = len(all_genes), dtype = fit_dtype)
for i, (gene, fitness) in enumerate(zip(all_genes, all_fitnesses)):
for par, val in gene.items():
par_matrix[par][i] = val
for k, stat in enumerate(fitness_weights.keys()):
fit_matrix[stat][i] = fitness[k]
np.savez_compressed(path + 'gen_%s_pars'%generation_number, par_matrix)
np.savez_compressed(path + 'gen_%s_fit'%generation_number, fit_matrix)
if all_ids: np.savez_compressed(path + 'gen_%s_dataids'%generation_number, all_ids)
