Similar to the dataset_simulated_annealing_optimizer worker, which takes a dataset as input, we can consider the curve_simulated_annealing_optimizer worker. It takes a curve as input and returns the optimal y value of the curve as output. A curve can be considered as a dataset with two columns with each representing one coordinate of the points on a…
Simulated annealing Simulated annealing is an optimization method to find the global optimum of the objective function. It is inspired by the process of metal annealing which heats the metal to a very high level and cools down in a controlled manner. In the SA algorithm, a random point is selected to start with. A…
Given two curves, a baseline and a candidate curve, how do we know how similar they are to each other? In machine learning, it is common to compare the predicted values (candidate curve) of a testing set to the true values (baseline curve) by RMSE (Root Mean Square Error). By comparing RMSE values of different…
Machine learning techniques (https://www.d3view.com/introduction-and-application-of-d3view-ml/) are becoming unprecedentedly popular. And it plays an important role in data analysis. It is critical to find the model that demonstrates the best performance. Intuitively, we can build a few different models with the data given and see which model gives the best score, either it being RMSE (root mean…
Optimization Decisions are made based on preferences. Either it is to minimize the monthly expense for a person living with a tight budget or to maximize the revenue of a restaurant for a business owner, we need to consider our preference or criteria (minimize or maximize) to form a solution. This process is called optimization…
In the previous post, we have discussed about how interpolation works in general and reviewed a few commonly used interpolation methods. All these methods only consider the adjacent a few points. This works fine most of the time. However, in reality, data points further away from the interpolation point may also have some impact on…
Introduction Given a set of points, we want to construct a curve that shares some specific patterns described by a group of curves. From the description of the problem, there are two pieces of information available to us. First, the points available (Prediction Points) are part of an unknown curve we want to reconstruct. Second,…
Machine learning is a data analysis technique that builds a model with the data and uses the model to predict. It learns from the data, identifies patterns and produces reliable predictions that help with decision making. As modern computational techniques advance, machine learning becomes more and more popular. There are two major groups of learning…
It is not unusual that due to various limitations, researchers can only collect limited number of samples. Meanwhile, for many analyses, we desire a higher resolution. In two-dimension case, we have X and Y coordinates of our points. We are interested in what happens in between any of the two points. We can draw a…
Battery data have many interesting properties. One of them is cycles. When we consider the whole range of the data, there are a few of the metrics we are interested in. For example, test net capacity and energy, test cumulative capacity and energy, etc. From these transformed curves, we can obtain some metrics that help…
