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def compute_similarity_weight(num_contacts: int, max_val: float=2.0, k: float=1.0) -> float:
    """
    Compute the similarity weighting factor based on the number of user-item interactions.

    Args:
        num_contacts (int): Number of user-item interactions.
 max_val (float): Maximum value for the similarity weighting factor.

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from typing import List
import numpy as np
from numpy.typing import ArrayLike
import scipy.sparse as sp
from sklearn.linear_model import ElasticNet
import warnings
from sklearn.exceptions import ConvergenceWarning
from tqdm import tqdm

class ColumnarView:

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import numpy as np
import scipy.sparse as sp
from sklearn.linear_model import ElasticNet
import warnings
from sklearn.exceptions import ConvergenceWarning

class SLIMElastic:
    """
    SLIMElastic is a sparse linear method for top-K recommendation, which learns
    a sparse aggregation coefficient matrix by solving an L1-norm and L2-norm

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// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:
// https://github.com/microsoft/vscode-dev-containers/tree/v0.231.6/containers/docker-existing-dockerfile
{
	"name": "AutoML",
	"image": "digdag/automl:3.8.1",
	"runArgs": ["--platform=linux/amd64"],
	"containerEnv": {
		// To enable integrated terminal
		"SHELL": "/bin/bash",
		"HOME": "/home/td-user"