/*
* xxHash - Extremely Fast Hash algorithm
* Header File
* Copyright (C) 2012-2021 Yann Collet
*
* BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php)
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are
* met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following disclaimer
* in the documentation and/or other materials provided with the
* distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
* OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* You can contact the author at:
* - xxHash homepage: https://www.xxhash.com
* - xxHash source repository: https://github.com/Cyan4973/xxHash
*/
/*!
* @mainpage xxHash
*
* xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed
* limits.
*
* It is proposed in four flavors, in three families:
* 1. @ref XXH32_family
* - Classic 32-bit hash function. Simple, compact, and runs on almost all
* 32-bit and 64-bit systems.
* 2. @ref XXH64_family
* - Classic 64-bit adaptation of XXH32. Just as simple, and runs well on most
* 64-bit systems (but _not_ 32-bit systems).
* 3. @ref XXH3_family
* - Modern 64-bit and 128-bit hash function family which features improved
* strength and performance across the board, especially on smaller data.
* It benefits greatly from SIMD and 64-bit without requiring it.
*
* Benchmarks
* ---
* The reference system uses an Intel i7-9700K CPU, and runs Ubuntu x64 20.04.
* The open source benchmark program is compiled with clang v10.0 using -O3 flag.
*
* | Hash Name | ISA ext | Width | Large Data Speed | Small Data Velocity |
* | -------------------- | ------- | ----: | ---------------: | ------------------: |
* | XXH3_64bits() | @b AVX2 | 64 | 59.4 GB/s | 133.1 |
* | MeowHash | AES-NI | 128 | 58.2 GB/s | 52.5 |
* | XXH3_128bits() | @b AVX2 | 128 | 57.9 GB/s | 118.1 |
* | CLHash | PCLMUL | 64 | 37.1 GB/s | 58.1 |
* | XXH3_64bits() | @b SSE2 | 64 | 31.5 GB/s | 133.1 |
* | XXH3_128bits() | @b SSE2 | 128 | 29.6 GB/s | 118.1 |
* | RAM sequential read | | N/A | 28.0 GB/s | N/A |
* | ahash | AES-NI | 64 | 22.5 GB/s | 107.2 |
* | City64 | | 64 | 22.0 GB/s | 76.6 |
* | T1ha2 | | 64 | 22.0 GB/s | 99.0 |
* | City128 | | 128 | 21.7 GB/s | 57.7 |
* | FarmHash | AES-NI | 64 | 21.3 GB/s | 71.9 |
* | XXH64() | | 64 | 19.4 GB/s | 71.0 |
* | SpookyHash | | 64 | 19.3 GB/s | 53.2 |
* | Mum | | 64 | 18.0 GB/s | 67.0 |
* | CRC32C | SSE4.2 | 32 | 13.0 GB/s | 57.9 |
* | XXH32() | | 32 | 9.7 GB/s | 71.9 |
* | City32 | | 32 | 9.1 GB/s | 66.0 |
* | Blake3* | @b AVX2 | 256 | 4.4 GB/s | 8.1 |
* | Murmur3 | | 32 | 3.9 GB/s | 56.1 |
* | SipHash* | | 64 | 3.0 GB/s | 43.2 |
* | Blake3* | @b SSE2 | 256 | 2.4 GB/s | 8.1 |
* | HighwayHash | | 64 | 1.4 GB/s | 6.0 |
* | FNV64 | | 64 | 1.2 GB/s | 62.7 |
* | Blake2* | | 256 | 1.1 GB/s | 5.1 |
* | SHA1* | | 160 | 0.8 GB/s | 5.6 |
* | MD5* | | 128 | 0.6 GB/s | 7.8 |
* @note
* - Hashes which require a specific ISA extension are noted. SSE2 is also noted,
* even though it is mandatory on x64.
* - Hashes with an asterisk are cryptographic. Note that MD5 is non-cryptographic
* by modern standards.
* - Small data velocity is a rough average of algorithm's efficiency for small
* data. For more accurate information, see the wiki.
* - More benchmarks and strength tests are found on the wiki:
* https://github.com/Cyan4973/xxHash/wiki
*
* Usage
* ------
* All xxHash variants use a similar API. Changing the algorithm is a trivial
* substitution.
*
* @pre
* For functions which take an input and length parameter, the following
* requirements are assumed:
* - The range from [`input`, `input + length`) is valid, readable memory.
* - The only exception is if the `length` is `0`, `input` may be `NULL`.
* - For C++, the objects must have the *TriviallyCopyable* property, as the
* functions access bytes directly as if it was an array of `unsigned char`.
*
* @anchor single_shot_example
* **Single Shot**
*
* These functions are stateless functions which hash a contiguous block of memory,
* immediately returning the result. They are the easiest and usually the fastest
* option.
*
* XXH32(), XXH64(), XXH3_64bits(), XXH3_128bits()
*
* @code{.c}
* #include <string.h>
* #include "xxhash.h"
*
* // Example for a function which hashes a null terminated string with XXH32().
* XXH32_hash_t hash_string(const char* string, XXH32_hash_t seed)
* {
* // NULL pointers are only valid if the length is zero
* size_t length = (string == NULL) ? 0 : strlen(string);
* return XXH32(string, length, seed);
* }
* @endcode
*
* @anchor streaming_example
* **Streaming**
*
* These groups of functions allow incremental hashing of unknown size, even
* more than what would fit in a size_t.
*
* XXH32_reset(), XXH64_reset(), XXH3_64bits_reset(), XXH3_128bits_reset()
*
* @code{.c}
* #include <stdio.h>
* #include <assert.h>
* #include "xxhash.h"
* // Example for a function which hashes a FILE incrementally with XXH3_64bits().
* XXH64_hash_t hashFile(FILE* f)
* {
* // Allocate a state struct. Do not just use malloc() or new.
* XXH3_state_t* state = XXH3_createState();
* assert(state != NULL && "Out of memory!");
* // Reset the state to start a new hashing session.
* XXH3_64bits_reset(state);
* char buffer[4096];
* size_t count;
* // Read the file in chunks
* while ((count = fread(buffer, 1, sizeof(buffer), f)) != 0) {
* // Run update() as many times as necessary to process the data
* XXH3_
没有合适的资源?快使用搜索试试~ 我知道了~
基于卷积神经网络(CNN)的岩石图像分类器
共2000个文件
json:1120个
h:495个
js:291个
需积分: 5 3 下载量 18 浏览量
2024-05-31
08:58:40
上传
评论 1
收藏 770.35MB RAR 举报
温馨提示
基于卷积神经网络(CNN)的岩石图像分类。有一岩石图片数据集,共300张岩石图片,图片尺寸224x224。岩石种类有砾岩(Conglomerate)、安山岩(Andesite)、花岗岩(Granite)、石灰岩(Limestone)、石英岩(Quartzite)和5种,每种岩石图片各50张,共250张。请选择合适模型对该数据集进行建模,训练优化模型并给出模型评估指标,再利用GUI框架开发岩石图片分类界面。
资源推荐
资源详情
资源评论
收起资源包目录
基于卷积神经网络(CNN)的岩石图像分类器 (2000个子文件)
style.min.css 261KB
font-awesome.css 37KB
jquery-ui.min.css 31KB
font-awesome.min.css 30KB
ipython.min.css 29KB
codemirror.css 9KB
index.css 4KB
bootstrap-tour.min.css 2KB
ie6.css 726B
override.css 325B
plot_directive.css 318B
print.css 303B
graphviz.css 299B
custom.css 135B
default.css 28B
xxhash.h 254KB
date.h 240KB
bpacking64_default.h 198KB
bpacking_default.h 105KB
type.h 93KB
tz.h 86KB
async_generator.h 78KB
api_scalar.h 66KB
type_traits.h 53KB
safe-math.h 48KB
properties.h 46KB
options.h 37KB
array_nested.h 36KB
exec_plan.h 36KB
random.h 35KB
types.h 33KB
hashing.h 33KB
builder_binary.h 33KB
future.h 32KB
builder_nested.h 31KB
kernel.h 31KB
test_util.h 31KB
rle_encoding.h 31KB
value_parsing.h 30KB
api_vector.h 29KB
scalar.h 28KB
builder_dict.h 28KB
types.h 25KB
sparse_tensor.h 25KB
thread_pool.h 24KB
scanner.h 24KB
reader.h 24KB
double-to-string.h 24KB
gtest_util.h 24KB
buffer.h 23KB
filesystem.h 23KB
data.h 23KB
ree_util.h 22KB
type_fwd.h 22KB
formatting.h 22KB
column_reader.h 22KB
extension_set.h 21KB
metadata.h 21KB
builder_primitive.h 21KB
test_util.h 20KB
bit_block_counter.h 20KB
file_base.h 20KB
message.h 20KB
dataset.h 20KB
lib_api.h 20KB
lib_api.h 20KB
encryption.h 20KB
async_util.h 20KB
writer.h 19KB
basic_decimal.h 19KB
light_array.h 18KB
iterator.h 18KB
stl.h 18KB
schema.h 18KB
exec.h 18KB
result.h 18KB
bitmap.h 18KB
buffer_builder.h 17KB
bit_stream_utils.h 17KB
api_aggregate.h 17KB
matchers.h 17KB
partition.h 17KB
client.h 17KB
encoding.h 17KB
bit_run_reader.h 17KB
file_parquet.h 17KB
serde.h 16KB
status.h 16KB
page_index.h 16KB
function.h 16KB
utils.h 16KB
ieee.h 15KB
reader.h 15KB
hash_join_dict.h 15KB
statistics.h 15KB
bloom_filter.h 15KB
s3fs.h 15KB
converter.h 15KB
small_vector.h 15KB
table.h 15KB
共 2000 条
- 1
- 2
- 3
- 4
- 5
- 6
- 20
资源评论
张謹礧
- 粉丝: 2w+
- 资源: 251
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
- 课程设计项目:python+QT实现的小型编译器.zip
- (源码)基于AVR ATmega644的智能卡AES解密系统.zip
- (源码)基于C++插件框架的计算与打印系统.zip
- (源码)基于Spring Boot和Vue的苍穹外卖管理系统.zip
- (源码)基于wxWidgets库的QMiniIDE游戏开发环境管理系统.zip
- 通过C++实现原型模式(Prototype Pattern).rar
- 学习记录111111111111111111111111
- 通过java实现原型模式(Prototype Pattern).rar
- 通过python实现原型模式(Prototype Pattern).rar
- xiefrnsdklmkds
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功