load_breast_cancer(return_X_y=True) # Create training and test split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y) sc = StandardScaler() X_train_std = sc.fit_transform(X_train) X_test_std = sc.transform(X_test) data...
model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn import datasets, metrics from mealpy import FloatVar, StringVar, IntegerVar, BoolVar, MixedSetVar, SMA, Problem # Load the data set; In this example, the breast cancer dataset is loaded. X, y...
import numpy as np from mealpy import BinaryVar, WOA, Problem # Define the problem parameters num_patients = 50 # Number of patients num_resources = 10 # Number of resources (room) # Define the patient waiting time matrix (randomly generated for the example) # Why? May be, doctors need...
model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn import datasets, metrics from mealpy import FloatVar, StringVar, IntegerVar, BoolVar, MixedSetVar, SMA, Problem # Load the data set; In this example, the breast cancer dataset is loaded. X, y...
import numpy as np from mealpy import BinaryVar, WOA, Problem # Define the problem parameters num_patients = 50 # Number of patients num_resources = 10 # Number of resources (room) # Define the patient waiting time matrix (randomly generated for the example) # Why? May be, doctors need...
An Artificial Intelligence Approach to Nutritional Meal Planning for Cancer Patients. In Artificial Intelligence Perspectives and Applications; Springer: Berlin/Heidelberg, Germany, 2015; pp. 215–224. [Google Scholar] Espín, V.; Hurtado, M.V.; Noguera, M. Nutrition for Elder Care: A nutritional...