Category : optimization

I have a dataset with thousands of cancer patients with columns saying what is the probability of surviving based on an specific treatment. Please see the table below I want to create an optimization in pyomo to return what is the best option for each patient maximizing the overall surviving probability. So, I need something ..

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I’m implementing Bayesian Optimization scheme from scratch, and when I’m trying to minimize the acquisition function by using scipy.optimize.minimize, I get the following error: for x_start in (np.random.random((self.batch_size, self.x_init.reshape(2,-1).shape[1])) * self.scale): response = minimize(fun=self.acquisition_function, x0=x_start, method = ‘L-BFGS-B’) ValueError: `f0` passed has more than 1 dimension Here is the code: class BayesianOptimizer: def __init__(self, target_func, ..

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Can someone help me in optimizing the below code. power = [2,3,2,1] noElement = len(power) result = 0 for i in range(noElement): sum = 0 minEleme = float(‘inf’) for j in range(i, noElement): minEleme = min(minEleme,power[j]) sum+=power[j] result += (minEleme*sum)%1000000007 print("min : ",minEleme,"sum : ",sum,"result : " ,result) print(result) Source: Python..

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