Technologies are generally described for systems, devices and methods relating to determining weights in a machine learning environment. In some examples, a training distribution of training data may be identified, information about a test distribution of test data, and a coordinate of the training data and the test data may be identified. Differences between the test distribution and the training distribution may be determined, for the coordinate. A weight importance parameter may be identified, for the coordinate. A processor may calculate weights based on the differences, and based on the weight importance parameter. The weights may be adapted to cause the training distribution to conform to the test distribution at a degree of conformance. The degree of conformance may be based on the weight importance parameter.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method to determine weights 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; identifying, by the processor, a weight importance parameter for the coordinate; and calculating, by the processor, weights based on the differences and based on the weight importance parameter, wherein the weights are adapted to cause the training distribution to conform to the test distribution at a degree of conformance, wherein the degree of conformance is based on the weight importance parameter. 2. The method of claim 1, wherein the coordinate is a first coordinate, the weight importance parameter is a first weight importance parameter, and the method further comprises: identifying, by the processor, a second coordinate of the training data and the test data; determining, by the processor, for the second coordinate, differences between the test distribution and the training distribution; identifying, by the processor, a second weight importance parameter for the second coordinate, wherein the second weight importance parameter is different from the first weight importance parameter; and wherein calculating the weights is further based on the second weight importance parameter, and wherein the degree of conformance is further based on the second weight importance parameter. 3. The method of claim 1, further comprising: generating a function based on the training data 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. 4. The method of claim 3, wherein the labels include at least one of recommendations, classifications, predictions, and determinations. 5. The method of claim 1, wherein: the training data is generated at a first instance of time; and the test data is generated at a second instance of time, wherein the second instance of time is later than the first instance of time. 6. The method of claim 1, wherein calculating the weights further comprises: iteratively determining differences between the training data and the test data, and wherein the weights are calculated based on a convergent value of the differences between the training data and the test data. 7. 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 calculating the weights is further based on the number of points in the training data and a number of the bins. 8. 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 calculating the weights is further based on the number of the first and second points which are located in the bins. 9. A method to determine weights 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; identifying, by the processor, a first weight importance parameter for the coordinate; calculating, by the processor, first weights based on the differences and based on the first weight importance parameter, wherein the first weights are adapted to cause the training distribution to conform to the test distribution at a first degree of conformance, wherein the first degree of conformance is based on the first weight importance parameter; identifying, by the processor, a second weight importance parameter for the coordinate; and calculating, by the processor, second weights based on the differences and based on the second weight importance parameter, wherein the second weights are adapted to cause the training distribution to conform to the test distribution at a second degree of conformance, wherein the second degree of conformance is based on the second weight importance parameter, and wherein the second degree of conformance is different from the first degree of conformance. 10. The method of claim 9, wherein the coordinate is a first coordinate, and the method further comprises: identifying a second coordinate of the training data and test data; identifying, by the processor, a third weight importance parameter for the second coordinate, wherein the third weight importance parameter is different from the first and second weight importance parameters; and wherein calculating the first weights and the second weights for the training data is further based, at least in part, on the third weight importance parameter. 11. The method of claim 9, further comprising: generating a function based on the training data 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. 12. The method of claim 11, wherein the labels include at least one of recommendations, classifications, predictions, and determinations. 13. The method of claim 9, wherein: the training data is generated at a first instance of time; and the test data is generated at a second instance of time, wherein the second instance of time is later than the first instance of time. 14. The method of claim 9, wherein calculating the weights further comprises: iteratively determining differences between the training data and the test data, and wherein the weights are calculated based on a convergent value of the differences between the training data and the test data. 15. The method of claim 9, 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 calculating the weights is further based on the number of points in the training data and a number of the bins. 16. A machine learning system, comprising: a processor; and a memory configured to be in communication with the processor, wherein the memory includes training data, wherein the training data comprises first points; and 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; identify a weight importance parameter for the coordinate; compare the first frequency and the second frequency; and calculate the weight for the training data, based at least in part on the comparison of the first and second frequencies, and based on the weight importance parameter, wherein the weight is adapted to cause the training data to conform to the test data at a degree of conformance, wherein the degree of conformance is based on the weight importance parameter; and the memory further effective to store the weight. 17. The machine learning system of claim 16, 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, in accordance with the degree of conformance. 18. The machine learning system of claim 16, wherein to compare the frequencies, 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 convergent frequency value in the memory. 19. The machine learning system of claim 16, 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; generate a function based at least in part on the weight and the training data; and store the function in the memory. 20. The machine learning system of claim 16, wherein the processor is further effective to calculate the weights based on a number of the first and second points which are located in the bins.