20 April 2020 04:37:06 PM SVD_SNOWFALL_TEST C++ version Test the SVD_SNOWFALL library. SVD_SNOWFALL_TEST01 Read, process, and return snowfall data in "snowfall.txt". Number of data rows M = 10 Number of data columns N = 129 Data has been read from the file. SVD_SNOWFALL_TEST02 Look at the singular values. If the singular values are close, then the data is well spread out. If the singular values decay rapidly, then the data exhibits patterns, or is constrained to a lower-dimensional subspace. The singular values: 0: 22306.9 1: 672.5 2: 240.764 3: 179.584 4: 148.676 5: 132.003 6: 86.53 7: 38.8972 8: 24.2869 9: 9.98088 Created data file "singular_values_data.txt". Created command file "singular_values_commands.txt". The cumulative energy: 0: 0 1: 0.998812 2: 0.99972 3: 0.999836 4: 0.999901 5: 0.999946 6: 0.999981 7: 0.999996 8: 0.999999 9: 1 10: 1 SVD_SNOWFALL_TEST03 Compute the rank 1 through rank 5 approximations to the data. Compare each of these to the 2012 snowfall data. Created data file "approx_data.txt". Created command file 'approx_commands.txt'. SVD_SNOWFALL_TEST04 Look at the first 6 modes in the U matrix. Each of these represents a pattern for snowfall over a year. The first mode is the pattern that is strongest in the data. Created data file "umode_data.txt". Created command file 'umode_commands.txt'. SVD_SNOWFALL_TEST05 Look at the first 6 modes in the V matrix. Each of these represents a pattern shared by all the months, and extending across the 123 sampling years. Created data file "vmode_data.txt". Created command file 'vmode_commands.txt'. SVD_SNOWFALL_TEST Normal end of execution. 20 April 2020 04:37:06 PM