As mg007 suggested, some of the scipy.optimize routines allow for a callback function (unfortunately leastsq does not permit this at the moment). Below is an example using the “fmin_bfgs” routine where I use a callback function to display the current value of the arguments and the value of the objective function at each iteration. import … Read more
To approach this problem, I would use an integer programming framework and define three sets of decision variables: x_ij: A binary indicator variable for whether we build a bridge at water location (i, j). y_ijbcn: A binary indicator for whether water location (i, j) is the n^th location linking island b to island c. l_bc: … Read more
Note: the code behind this answer can be found here. Suppose we have some data sampled from two different groups, red and blue: Here, we can see which data point belongs to the red or blue group. This makes it easy to find the parameters that characterise each group. For example, the mean of the … Read more
Breaking symmetry is essential here, and not for the reason of performance. Imagine first 2 layers of multilayer perceptron (input and hidden layers): During forward propagation each unit in hidden layer gets signal: That is, each hidden unit gets sum of inputs multiplied by the corresponding weight. Now imagine that you initialize all weights to … Read more
The lower the loss, the better a model (unless the model has over-fitted to the training data). The loss is calculated on training and validation and its interperation is how well the model is doing for these two sets. Unlike accuracy, loss is not a percentage. It is a summation of the errors made for … Read more
What is NP? NP is the set of all decision problems (questions with a yes-or-no answer) for which the ‘yes’-answers can be verified in polynomial time (O(nk) where n is the problem size, and k is a constant) by a deterministic Turing machine. Polynomial time is sometimes used as the definition of fast or quickly. … Read more