Technologies are generally described for systems, devices and methods relating to a machine learning environment. In some examples, a processor may identify a training distribution of a training data. The processor may identify information about a test distribution of a test data. The processor may identify a coordinate of the training data and the test data. The processor may determine, for the coordinate, differences between the test distribution and the training distribution. The processor may determine weights based on the differences. The weights may be adapted to cause the training distribution to conform to the test distribution when the weights are applied to the training distribution.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method in a machine learning environment, the method comprising: identifying, by a processor, a training distribution of a training data; identifying, by the processor, information about a test distribution of a test data; identifying, by the processor, a coordinate of the training data and the test data; determining, by the processor, for the coordinate, differences between the test distribution and the training distribution; and determining, by the processor, weights based on the differences, wherein the weights are adapted to cause the training distribution to conform to the test distribution when the weights are applied to the training distribution. 2. The method of claim 1, further comprising: generating a function based on the training distribution and the weights; applying the test data to the function as an input; and generating labels, in response to the application of the test data to the function. 3. The method of claim 2, wherein the labels include at least one of recommendations, classifications, predictions, and determinations. 4. The method of claim 1, wherein: the training data is generated at a first instance in time; and the test data is generated at a second instance in time, wherein the second instance in time is later than the first instance in time. 5. The method of claim 1, wherein determining the weights further comprises: iteratively determining differences between the training distribution and the test distribution, and wherein the weights are determined based on a convergent value of the differences between the training distribution and the test distribution. 6. The method of claim 1, wherein the training data includes a number of points, the coordinate includes a range of values in a coordinate space, and the method further comprises: dividing the range of values in the coordinate space into bins; and wherein determining the weights is further based on the number of points in the training data and a number of the bins. 7. The method of claim 1, wherein the test data and the training data include first and second points, respectively, the coordinate includes a range of values in a coordinate space and the method further comprises: dividing the range of values in the coordinate space into bins; and wherein determining the weights is further based on the number of the first and second points which are located in the bins. 8. A method to determine a weight for training data, the method comprising, by a processor: identifying first points of the training data; identifying information about a test data, wherein the test data includes second points; identifying a coordinate of the first and second points, wherein the coordinate includes a range of values in a coordinate space; dividing the range of values in the coordinate space into bins, wherein respective bins define subsets of the range of values; determining a first frequency, wherein the first frequency relates to a first percentage of the first points being located within a particular bin; determining a second frequency, wherein the second frequency relates to a second percentage of the second points being located within the particular bin; comparing the first frequency and the second frequency; and determining the weight for the training data, based at least in part on the comparison of the first and second frequencies. 9. The method of claim 8, wherein: the first points follow a training distribution; the second points follow a test distribution; and the weight is effective to conform a particular point in the training distribution to a particular point in the test distribution. 10. The method of claim 8, wherein comparing the first and second frequency includes: identifying a first comparison value; comparing frequency values of the test data and the training data in respective bins and coordinates, to produce a difference value; updating the first comparison value to produce a second comparison value based on the difference value; and iteratively repeating the identifying a first comparison value, comparing frequency values of the test data and the training data in respective bins and coordinates, to produce a difference value, and updating the first comparison value to produce a second comparison value based on the difference value until the second comparison value converges. 11. The method of claim 10, wherein updating the first comparison value to produce a second comparison value based on the difference value comprises: adding a fraction of the difference value to the first comparison value to produce the second comparison value. 12. The method of claim 8, wherein determining the weight is further based on a number of the first points and a number of the bins. 13. The method of claim 8, wherein determining the weight is further based on a number of the first and second points which are located in the bins. 14. A computing device, comprising: a processor; and a memory configured to be in communication with the processor, the memory effective to store training data, wherein the training data comprises first points; the memory further effective to store test data, wherein the test data comprises second points; the processor effective to: identify a coordinate of the first and second points, wherein the coordinate includes a range of values in a coordinate space; divide the range of values in the coordinate space into bins, wherein respective bins define subsets of the range of values; determine a first frequency, wherein the first frequency relates to a first percentage of the first points which are located within a particular bin; determine a second frequency, wherein the second frequency relates to a second percentage of the second points which are located within the particular bin; compare the first frequency and the second frequency; and determine a weight for the training data, based at least in part on the comparison of the first and second frequencies; and the memory further effective to store the weight. 15. The computing device of claim 14, wherein: the first points follow a training distribution; the second points follow a test distribution; and the weight is effective to conform a particular point in the training distribution to a particular point in the test distribution. 16. The computing device of claim 14, wherein the processor is further effective to: identify a first comparison value; compare frequency values of the test data and the training data in respective bins and coordinates, to produce a difference value; update the first comparison value to produce a second comparison value based on the difference value; iteratively repeat the identification of a first comparison value, the comparison of frequency values of the test data and the training data in respective bins and coordinates, to produce a difference value, and the update of the first comparison value to produce a second comparison value based on the difference value, until the second comparison value converges; and store the converged second comparison value in the memory. 17. The computing device of claim 16, wherein the processor is further effective to update the first comparison value to produce a second comparison value based on the difference value, by addition of a fraction of the difference value to the first comparison value. 18. The computing device of claim 14, further comprising a machine learning module; wherein the processor is further effective to: send the weight and the training data to the machine learning module; in response, the machine learning module is effective to: receive the weight and the training data from the processor; and generate a function based at least in part on the weight and the training data; and store the function in the memory. 19. The computing device of claim 14, wherein the processor is effective to determine the weight based on a number of the first points and a number of the bins. 20. The computing device of claim 14, wherein the processor is further effective to determine the weights based on a number of the first and second points which are located in the bins.