Lines Matching full:samples

51  * structure, you can train a dictionary on ahead of time on some samples of
53  * present across samples. This can vastly improve compression ratio.
79  * repeated content that is common across many samples.
91  * Gather samples from your use case. These samples should be similar to each
95  * Pass those samples to `ZDICT_trainFromBuffer()` and that will train your
102  * either passed too few samples, or a dictionary would not be effective
104  * if it doesn't say too few samples, then a dictionary would not be effective.
118  * How many samples should I provide to the dictionary builder?
122  * in samples. A few thousand should suffice. Having too few samples
123  * can hurt the dictionaries effectiveness. Having more samples will
125  * samples can slow down the dictionary builder.
154  * provide some samples of the data. It will add the zstd header to the
180  *  Train a dictionary from an array of samples.
183  *  Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
188  *  Note:  Dictionary training will fail if there are not enough samples to construct a
189  *         dictionary, or if most of the samples are too small (< 8 bytes being the lower limit).
191 …*         would've been ineffective anyways. If you believe your samples would benefit from a dict…
196 …*        In general, it's recommended to provide a few thousands samples, though this can vary a l…
197 …*        It's recommended that total size of all samples be about ~x100 times the target size of d…
217  * Given a custom content as a basis for dictionary, and a set of samples,
221  * Samples must be stored concatenated in a flat buffer `samplesBuffer`,
223  * sample in order. The samples are used to construct the statistics, so they
247  *         * Not enough samples
248  *         * Samples are uncompressible
249  *         * Samples are all exactly the same
287 …samples used for training: Only used for optimization : the first nbSamples * splitPoint samples w…
299 …samples used for training: Only used for optimization : the first nbSamples * splitPoint samples w…
308  *  Train a dictionary from an array of samples using the COVER algorithm.
309  *  Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
318 …*        In general, it's recommended to provide a few thousands samples, though this can vary a l…
319 …*        It's recommended that total size of all samples be about ~x100 times the target size of d…
349  *  Train a dictionary from an array of samples using a modified version of COVER algorithm.
350  *  Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
361 …*        In general, it's recommended to provide a few thousands samples, though this can vary a l…
362 …*        It's recommended that total size of all samples be about ~x100 times the target size of d…
398  *  Train a dictionary from an array of samples.
399  *  Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
408 …*        In general, it's recommended to provide a few thousands samples, though this can vary a l…
409 …*        It's recommended that total size of all samples be about ~x100 times the target size of d…