![]() Igualmente trabajaremos con la librería () para realizar el proceso de recomendación que consta de los siguientes pasos: # Utilizaremos en el programa librerías que nos permitiran cargar y limpiar los datos con el fin de tener la mejor calidad en la información. # Recomendación de libros a usuarios de Cross Booking Mesh.write('nanopt_full_period_2domain_all_connections.gmsh', file_format="gmsh22", binary=False) # Insert information about periodicity for HPGEMįrom nanomesh.periodic_utils import insert_periodic_info Mesh = pygalmesh.generate_periodic_mesh_multiple_domains( # Afterwards, scale the mesh to obtain the correct dimension # Generates `dummies_post_treatment.xyz`, `dummies_pre_treatment.xyz` # The resulting mesh will be deformed as our domain is not cubic ![]() # Instantiate the pore and visualize a section of the data Plt.scatter(*X.T, s=11, alpha= 1)įrom nanomesh.structures import Pore3D, FullCube, XDIM, YDIM, ZDIM Plt.fill_between(xd, yd, ymax, color='tab:orange', alpha = 0.2) Plt.fill_between(xd, yd, ymin, color='tab:blue', alpha = 0.2) X_train = np.zeros((X.shape,X.shape + 1)) Print("The cost after Iteration ".format(j + 1,C)) ''' x is the data vector appended with 1 for bias '''ĭef Gradient_Descent(iterations, X_train, y_train, learning_rate ): X, y = make_blobs(n_samples=100, centers=,], random_state=20) ![]() # Logistic Regression Using Gradient Descent ![]() Yield indices, indicesįor (train1, test1), (train2, test2) in zip(cv1.split(X, y), cv2.split(X, y)):Ĭv1 = KFold(n_splits=5, shuffle=True, random_state=0)Ĭv2 = skKFold(n_splits=5, shuffle=True, random_state=0) Test_mask = np.zeros(X.shape, dtype=bool)įor test_index in self._iter_test_masks(X, y): Yield indicesįor test_index in self._iter_test_indices(X, y): Rng = np.random.RandomState(self.random_state)įold_sizes = np.full(self.n_splits, X.shape // self.n_splits)įold_sizes % self.n_splits] += 1 From sklearn.model_selection import KFold as skKFoldĭef _init_(self, n_splits=5, shuffle=False, random_state=0): ![]()
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