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TensorFlow is a framework for building machine learning projects that is pretty easy to use. However, that doesn’t mean it’s always easy to set up, especially when you are playing with the bleeding edge features.

This post if for future reference

During the last few years, I have run into situations where TensorFlow won’t work in certain environments multiple times. Every time that happens, I had to spend hours searching the internet for fragmented information and extra hours to put the pieces together. This time, I decided to write up a detailed tutorial to save future situations where nothing works.

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A Pure PHP Implementation Of The MessagePack Serialization Format

msgpack.php

A pure PHP implementation of the MessagePack serialization format.

Features

• Fully compliant with the latest MessagePack specification
• Supports streaming unpacking
• Supports unsigned 64-bit integers handling
• Supports object serialization
• Fully tested
• Relatively fast

Installation

The recommended way to install the library is through Composer:

composer require rybakit/msgpack

Usage

Packing

To pack values you can either use an instance of a Packer:

$packer = new Packer();
$packed = $packer->pack($value);

or call a static method on the MessagePack class:

$packed = MessagePack::pack($value);

In the examples above, the method pack automatically packs a value depending on its type. However, not all PHP types can be uniquely translated to MessagePack types. For example, the MessagePack format defines map and array types, which are represented by a single array type in PHP. By default, the packer will pack a PHP array as a MessagePack array if it has sequential numeric keys, starting from 0 and as a MessagePack map otherwise:

$mpArr1 = $packer->pack([1, 2]);               // MP array [1, 2]
$mpArr2 = $packer->pack([0 => 1, 1 => 2]);     // MP array [1, 2]
$mpMap1 = $packer->pack([0 => 1, 2 => 3]);     // MP map {0: 1, 2: 3}
$mpMap2 = $packer->pack([1 => 2, 2 => 3]);     // MP map {1: 2, 2: 3}
$mpMap3 = $packer->pack(['a' => 1, 'b' => 2]); // MP map {a: 1, b: 2}

However, sometimes you need to pack a sequential array as a MessagePack map. To do this, use the packMap method:

$mpMap = $packer->packMap([1, 2]); // {0: 1, 1: 2}

Here is a list of type-specific packing methods:

$packer->packNil();           // MP nil
$packer->packBool(true);      // MP bool
$packer->packInt(42);         // MP int
$packer->packFloat(M_PI);     // MP float (32 or 64)
$packer->packFloat32(M_PI);   // MP float 32
$packer->packFloat64(M_PI);   // MP float 64
$packer->packStr('foo');      // MP str
$packer->packBin("\x80");     // MP bin
$packer->packArray([1, 2]);   // MP array
$packer->packMap(['a' => 1]); // MP map
$packer->packExt(1, "\xaa");  // MP ext

Check the "Custom types" section below on how to pack custom types.

Packing options

The Packer object supports a number of bitmask-based options for fine-tuning the packing process (defaults are in bold):

Name	Description
FORCE_STR	Forces PHP strings to be packed as MessagePack UTF-8 strings
FORCE_BIN	Forces PHP strings to be packed as MessagePack binary data
DETECT_STR_BIN	Detects MessagePack str/bin type automatically
FORCE_ARR	Forces PHP arrays to be packed as MessagePack arrays
FORCE_MAP	Forces PHP arrays to be packed as MessagePack maps
DETECT_ARR_MAP	Detects MessagePack array/map type automatically
FORCE_FLOAT32	Forces PHP floats to be packed as 32-bits MessagePack floats
FORCE_FLOAT64	Forces PHP floats to be packed as 64-bits MessagePack floats

The type detection mode (DETECT_STR_BIN/DETECT_ARR_MAP) adds some overhead which can be noticed when you pack large (16- and 32-bit) arrays or strings. However, if you know the value type in advance (for example, you only work with UTF-8 strings or/and associative arrays), you can eliminate this overhead by forcing the packer to use the appropriate type, which will save it from running the auto-detection routine. Another option is to explicitly specify the value type. The library provides 2 auxiliary classes for this, Map and Bin. Check the "Custom types" section below for details.

Examples:

// detect str/bin type and pack PHP 64-bit floats (doubles) to MP 32-bit floats
$packer = new Packer(PackOptions::DETECT_STR_BIN | PackOptions::FORCE_FLOAT32);

// these will throw MessagePack\Exception\InvalidOptionException
$packer = new Packer(PackOptions::FORCE_STR | PackOptions::FORCE_BIN);
$packer = new Packer(PackOptions::FORCE_FLOAT32 | PackOptions::FORCE_FLOAT64);

Unpacking

To unpack data you can either use an instance of a BufferUnpacker:

$unpacker = new BufferUnpacker();

$unpacker->reset($packed);
$value = $unpacker->unpack();

or call a static method on the MessagePack class:

$value = MessagePack::unpack($packed);

If the packed data is received in chunks (e.g. when reading from a stream), use the tryUnpack method, which attempts to unpack data and returns an array of unpacked messages (if any) instead of throwing an InsufficientDataException:

while ($chunk = ...) {
    $unpacker->append($chunk);
    if ($messages = $unpacker->tryUnpack()) {
        return $messages;
    }
}

If you want to unpack from a specific position in a buffer, use seek:

$unpacker->seek(42); // set position equal to 42 bytes
$unpacker->seek(-8); // set position to 8 bytes before the end of the buffer

To skip bytes from the current position, use skip:

$unpacker->skip(10); // set position to 10 bytes ahead of the current position

To get the number of remaining (unread) bytes in the buffer:

$unreadBytesCount = $unpacker->getRemainingCount();

To check whether the buffer has unread data:

$hasUnreadBytes = $unpacker->hasRemaining();

If needed, you can remove already read data from the buffer by calling:

$releasedBytesCount = $unpacker->release();

With the read method you can read raw (packed) data:

$packedData = $unpacker->read(2); // read 2 bytes

Besides the above methods BufferUnpacker provides type-specific unpacking methods, namely:

$unpacker->unpackNil();   // PHP null
$unpacker->unpackBool();  // PHP bool
$unpacker->unpackInt();   // PHP int
$unpacker->unpackFloat(); // PHP float
$unpacker->unpackStr();   // PHP UTF-8 string
$unpacker->unpackBin();   // PHP binary string
$unpacker->unpackArray(); // PHP sequential array
$unpacker->unpackMap();   // PHP associative array
$unpacker->unpackExt();   // PHP MessagePack\Type\Ext object

Unpacking options

The BufferUnpacker object supports a number of bitmask-based options for fine-tuning the unpacking process (defaults are in bold):

Name	Description
BIGINT_AS_STR	Converts overflowed integers to strings [1]
BIGINT_AS_GMP	Converts overflowed integers to GMP objects [2]
BIGINT_AS_DEC	Converts overflowed integers to Decimal\Decimal objects [3]

1. The binary MessagePack format has unsigned 64-bit as its largest integer data type, but PHP does not support such integers, which means that an overflow can occur during unpacking.

2. Make sure the GMP extension is enabled.

3. Make sure the Decimal extension is enabled.

Examples:

$packedUint64 = "\xcf"."\xff\xff\xff\xff"."\xff\xff\xff\xff";

$unpacker = new BufferUnpacker($packedUint64);
var_dump($unpacker->unpack()); // string(20) "18446744073709551615"

$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_GMP);
var_dump($unpacker->unpack()); // object(GMP) {...}

$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_DEC);
var_dump($unpacker->unpack()); // object(Decimal\Decimal) {...}

Custom types

In addition to the basic types, the library provides functionality to serialize and deserialize arbitrary types. This can be done in several ways, depending on your use case. Let's take a look at them.

Type objects

If you need to serialize an instance of one of your classes into one of the basic MessagePack types, the best way to do this is to implement the CanBePacked interface in the class. A good example of such a class is the Map type class that comes with the library. This type is useful when you want to explicitly specify that a given PHP array should be packed as a MessagePack map without triggering an automatic type detection routine:

$packer = new Packer();

$packedMap = $packer->pack(new Map([1, 2, 3]));
$packedArray = $packer->pack([1, 2, 3]);

More type examples can be found in the src/Type directory.

Type transformers

As with type objects, type transformers are only responsible for serializing values. They should be used when you need to serialize a value that does not implement the CanBePacked interface. Examples of such values could be instances of built-in or third-party classes that you don't own, or non-objects such as resources.

A transformer class must implement the CanPack interface. To use a transformer, it must first be registered in the packer. Here is an example of how to serialize PHP streams into the MessagePack bin format type using one of the supplied transformers, StreamTransformer:

$packer = new Packer(null, [new StreamTransformer()]);

$packedBin = $packer->pack(fopen('/path/to/file', 'r+'));

More type transformer examples can be found in the src/TypeTransformer directory.

Extensions

In contrast to the cases described above, extensions are intended to handle extension types and are responsible for both serialization and deserialization of values (types).

An extension class must implement the Extension interface. To use an extension, it must first be registered in the packer and the unpacker.

The MessagePack specification divides extension types into two groups: predefined and application-specific. Currently, there is only one predefined type in the specification, Timestamp.

Timestamp

The Timestamp extension type is a predefined type. Support for this type in the library is done through the TimestampExtension class. This class is responsible for handling Timestamp objects, which represent the number of seconds and optional adjustment in nanoseconds:

$timestampExtension = new TimestampExtension();

$packer = new Packer();
$packer = $packer->extendWith($timestampExtension);

$unpacker = new BufferUnpacker();
$unpacker = $unpacker->extendWith($timestampExtension);

$packedTimestamp = $packer->pack(Timestamp::now());
$timestamp = $unpacker->reset($packedTimestamp)->unpack();

$seconds = $timestamp->getSeconds();
$nanoseconds = $timestamp->getNanoseconds();

When using the MessagePack class, the Timestamp extension is already registered:

$packedTimestamp = MessagePack::pack(Timestamp::now());
$timestamp = MessagePack::unpack($packedTimestamp);

Application-specific extensions

In addition, the format can be extended with your own types. For example, to make the built-in PHP DateTime objects first-class citizens in your code, you can create a corresponding extension, as shown in the example. Please note, that custom extensions have to be registered with a unique extension ID (an integer from 0 to 127).

More extension examples can be found in the examples/MessagePack directory.

To learn more about how extension types can be useful, check out this article.

Exceptions

If an error occurs during packing/unpacking, a PackingFailedException or an UnpackingFailedException will be thrown, respectively. In addition, an InsufficientDataException can be thrown during unpacking.

An InvalidOptionException will be thrown in case an invalid option (or a combination of mutually exclusive options) is used.

Tests

Run tests as follows:

vendor/bin/phpunit

Also, if you already have Docker installed, you can run the tests in a docker container. First, create a container:

./dockerfile.sh | docker build -t msgpack -

The command above will create a container named msgpack with PHP 8.1 runtime. You may change the default runtime by defining the PHP_IMAGE environment variable:

PHP_IMAGE='php:8.0-cli' ./dockerfile.sh | docker build -t msgpack -

See a list of various images here.

Then run the unit tests:

docker run --rm -v $PWD:/msgpack -w /msgpack msgpack

Fuzzing

To ensure that the unpacking works correctly with malformed/semi-malformed data, you can use a testing technique called Fuzzing. The library ships with a help file (target) for PHP-Fuzzer and can be used as follows:

php-fuzzer fuzz tests/fuzz_buffer_unpacker.php

Performance

To check performance, run:

php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0030 ........ 0.0139
false ................ 0.0037 ........ 0.0144
true ................. 0.0040 ........ 0.0137
7-bit uint #1 ........ 0.0052 ........ 0.0120
7-bit uint #2 ........ 0.0059 ........ 0.0114
7-bit uint #3 ........ 0.0061 ........ 0.0119
5-bit sint #1 ........ 0.0067 ........ 0.0126
5-bit sint #2 ........ 0.0064 ........ 0.0132
5-bit sint #3 ........ 0.0066 ........ 0.0135
8-bit uint #1 ........ 0.0078 ........ 0.0200
8-bit uint #2 ........ 0.0077 ........ 0.0212
8-bit uint #3 ........ 0.0086 ........ 0.0203
16-bit uint #1 ....... 0.0111 ........ 0.0271
16-bit uint #2 ....... 0.0115 ........ 0.0260
16-bit uint #3 ....... 0.0103 ........ 0.0273
32-bit uint #1 ....... 0.0116 ........ 0.0326
32-bit uint #2 ....... 0.0118 ........ 0.0332
32-bit uint #3 ....... 0.0127 ........ 0.0325
64-bit uint #1 ....... 0.0140 ........ 0.0277
64-bit uint #2 ....... 0.0134 ........ 0.0294
64-bit uint #3 ....... 0.0134 ........ 0.0281
8-bit int #1 ......... 0.0086 ........ 0.0241
8-bit int #2 ......... 0.0089 ........ 0.0225
8-bit int #3 ......... 0.0085 ........ 0.0229
16-bit int #1 ........ 0.0118 ........ 0.0280
16-bit int #2 ........ 0.0121 ........ 0.0270
16-bit int #3 ........ 0.0109 ........ 0.0274
32-bit int #1 ........ 0.0128 ........ 0.0346
32-bit int #2 ........ 0.0118 ........ 0.0339
32-bit int #3 ........ 0.0135 ........ 0.0368
64-bit int #1 ........ 0.0138 ........ 0.0276
64-bit int #2 ........ 0.0132 ........ 0.0286
64-bit int #3 ........ 0.0137 ........ 0.0274
64-bit int #4 ........ 0.0180 ........ 0.0285
64-bit float #1 ...... 0.0134 ........ 0.0284
64-bit float #2 ...... 0.0125 ........ 0.0275
64-bit float #3 ...... 0.0126 ........ 0.0283
fix string #1 ........ 0.0035 ........ 0.0133
fix string #2 ........ 0.0094 ........ 0.0216
fix string #3 ........ 0.0094 ........ 0.0222
fix string #4 ........ 0.0091 ........ 0.0241
8-bit string #1 ...... 0.0122 ........ 0.0301
8-bit string #2 ...... 0.0118 ........ 0.0304
8-bit string #3 ...... 0.0119 ........ 0.0315
16-bit string #1 ..... 0.0150 ........ 0.0388
16-bit string #2 ..... 0.1545 ........ 0.1665
32-bit string ........ 0.1570 ........ 0.1756
wide char string #1 .. 0.0091 ........ 0.0236
wide char string #2 .. 0.0122 ........ 0.0313
8-bit binary #1 ...... 0.0100 ........ 0.0302
8-bit binary #2 ...... 0.0123 ........ 0.0324
8-bit binary #3 ...... 0.0126 ........ 0.0327
16-bit binary ........ 0.0168 ........ 0.0372
32-bit binary ........ 0.1588 ........ 0.1754
fix array #1 ......... 0.0042 ........ 0.0131
fix array #2 ......... 0.0294 ........ 0.0367
fix array #3 ......... 0.0412 ........ 0.0472
16-bit array #1 ...... 0.1378 ........ 0.1596
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.1865 ........ 0.2283
fix map #1 ........... 0.0725 ........ 0.1048
fix map #2 ........... 0.0319 ........ 0.0405
fix map #3 ........... 0.0356 ........ 0.0665
fix map #4 ........... 0.0465 ........ 0.0497
16-bit map #1 ........ 0.2540 ........ 0.3028
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.2372 ........ 0.2710
fixext 1 ............. 0.0283 ........ 0.0358
fixext 2 ............. 0.0291 ........ 0.0371
fixext 4 ............. 0.0302 ........ 0.0355
fixext 8 ............. 0.0288 ........ 0.0384
fixext 16 ............ 0.0293 ........ 0.0359
8-bit ext ............ 0.0302 ........ 0.0439
16-bit ext ........... 0.0334 ........ 0.0499
32-bit ext ........... 0.1845 ........ 0.1888
32-bit timestamp #1 .. 0.0337 ........ 0.0547
32-bit timestamp #2 .. 0.0335 ........ 0.0560
64-bit timestamp #1 .. 0.0371 ........ 0.0575
64-bit timestamp #2 .. 0.0374 ........ 0.0542
64-bit timestamp #3 .. 0.0356 ........ 0.0533
96-bit timestamp #1 .. 0.0362 ........ 0.0699
96-bit timestamp #2 .. 0.0381 ........ 0.0701
96-bit timestamp #3 .. 0.0367 ........ 0.0687
=============================================
Total                  2.7618          4.0820
Skipped                     4               4
Failed                      0               0
Ignored                     0               0

With JIT:

php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0005 ........ 0.0054
false ................ 0.0004 ........ 0.0059
true ................. 0.0004 ........ 0.0059
7-bit uint #1 ........ 0.0010 ........ 0.0047
7-bit uint #2 ........ 0.0010 ........ 0.0046
7-bit uint #3 ........ 0.0010 ........ 0.0046
5-bit sint #1 ........ 0.0025 ........ 0.0046
5-bit sint #2 ........ 0.0023 ........ 0.0046
5-bit sint #3 ........ 0.0024 ........ 0.0045
8-bit uint #1 ........ 0.0043 ........ 0.0081
8-bit uint #2 ........ 0.0043 ........ 0.0079
8-bit uint #3 ........ 0.0041 ........ 0.0080
16-bit uint #1 ....... 0.0064 ........ 0.0095
16-bit uint #2 ....... 0.0064 ........ 0.0091
16-bit uint #3 ....... 0.0064 ........ 0.0094
32-bit uint #1 ....... 0.0085 ........ 0.0114
32-bit uint #2 ....... 0.0077 ........ 0.0122
32-bit uint #3 ....... 0.0077 ........ 0.0120
64-bit uint #1 ....... 0.0085 ........ 0.0159
64-bit uint #2 ....... 0.0086 ........ 0.0157
64-bit uint #3 ....... 0.0086 ........ 0.0158
8-bit int #1 ......... 0.0042 ........ 0.0080
8-bit int #2 ......... 0.0042 ........ 0.0080
8-bit int #3 ......... 0.0042 ........ 0.0081
16-bit int #1 ........ 0.0065 ........ 0.0095
16-bit int #2 ........ 0.0065 ........ 0.0090
16-bit int #3 ........ 0.0056 ........ 0.0085
32-bit int #1 ........ 0.0067 ........ 0.0107
32-bit int #2 ........ 0.0066 ........ 0.0106
32-bit int #3 ........ 0.0063 ........ 0.0104
64-bit int #1 ........ 0.0072 ........ 0.0162
64-bit int #2 ........ 0.0073 ........ 0.0174
64-bit int #3 ........ 0.0072 ........ 0.0164
64-bit int #4 ........ 0.0077 ........ 0.0161
64-bit float #1 ...... 0.0053 ........ 0.0135
64-bit float #2 ...... 0.0053 ........ 0.0135
64-bit float #3 ...... 0.0052 ........ 0.0135
fix string #1 ....... -0.0002 ........ 0.0044
fix string #2 ........ 0.0035 ........ 0.0067
fix string #3 ........ 0.0035 ........ 0.0077
fix string #4 ........ 0.0033 ........ 0.0078
8-bit string #1 ...... 0.0059 ........ 0.0110
8-bit string #2 ...... 0.0063 ........ 0.0121
8-bit string #3 ...... 0.0064 ........ 0.0124
16-bit string #1 ..... 0.0099 ........ 0.0146
16-bit string #2 ..... 0.1522 ........ 0.1474
32-bit string ........ 0.1511 ........ 0.1483
wide char string #1 .. 0.0039 ........ 0.0084
wide char string #2 .. 0.0073 ........ 0.0123
8-bit binary #1 ...... 0.0040 ........ 0.0112
8-bit binary #2 ...... 0.0075 ........ 0.0123
8-bit binary #3 ...... 0.0077 ........ 0.0129
16-bit binary ........ 0.0096 ........ 0.0145
32-bit binary ........ 0.1535 ........ 0.1479
fix array #1 ......... 0.0008 ........ 0.0061
fix array #2 ......... 0.0121 ........ 0.0165
fix array #3 ......... 0.0193 ........ 0.0222
16-bit array #1 ...... 0.0607 ........ 0.0479
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.0749 ........ 0.0824
fix map #1 ........... 0.0329 ........ 0.0431
fix map #2 ........... 0.0161 ........ 0.0189
fix map #3 ........... 0.0205 ........ 0.0262
fix map #4 ........... 0.0252 ........ 0.0205
16-bit map #1 ........ 0.1016 ........ 0.0927
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.1096 ........ 0.1030
fixext 1 ............. 0.0157 ........ 0.0161
fixext 2 ............. 0.0175 ........ 0.0183
fixext 4 ............. 0.0156 ........ 0.0185
fixext 8 ............. 0.0163 ........ 0.0184
fixext 16 ............ 0.0164 ........ 0.0182
8-bit ext ............ 0.0158 ........ 0.0207
16-bit ext ........... 0.0203 ........ 0.0219
32-bit ext ........... 0.1614 ........ 0.1539
32-bit timestamp #1 .. 0.0195 ........ 0.0249
32-bit timestamp #2 .. 0.0188 ........ 0.0260
64-bit timestamp #1 .. 0.0207 ........ 0.0281
64-bit timestamp #2 .. 0.0212 ........ 0.0291
64-bit timestamp #3 .. 0.0207 ........ 0.0295
96-bit timestamp #1 .. 0.0222 ........ 0.0358
96-bit timestamp #2 .. 0.0228 ........ 0.0353
96-bit timestamp #3 .. 0.0210 ........ 0.0319
=============================================
Total                  1.6432          1.9674
Skipped                     4               4
Failed                      0               0
Ignored                     0               0

You may change default benchmark settings by defining the following environment variables:

Name	Default
MP_BENCH_TARGETS	pure_p,pure_u, see a list of available targets
MP_BENCH_ITERATIONS	100_000
MP_BENCH_DURATION	not set
MP_BENCH_ROUNDS	3
MP_BENCH_TESTS	-@slow, see a list of available tests

For example:

export MP_BENCH_TARGETS=pure_p
export MP_BENCH_ITERATIONS=1000000
export MP_BENCH_ROUNDS=5
# a comma separated list of test names
export MP_BENCH_TESTS='complex array, complex map'
# or a group name
# export MP_BENCH_TESTS='-@slow' // @pecl_comp
# or a regexp
# export MP_BENCH_TESTS='/complex (array|map)/'

Another example, benchmarking both the library and the PECL extension:

MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0031 ........ 0.0141 ...... 0.0055 ........ 0.0064
false ................ 0.0039 ........ 0.0154 ...... 0.0056 ........ 0.0053
true ................. 0.0038 ........ 0.0139 ...... 0.0056 ........ 0.0044
7-bit uint #1 ........ 0.0061 ........ 0.0110 ...... 0.0059 ........ 0.0046
7-bit uint #2 ........ 0.0065 ........ 0.0119 ...... 0.0042 ........ 0.0029
7-bit uint #3 ........ 0.0054 ........ 0.0117 ...... 0.0045 ........ 0.0025
5-bit sint #1 ........ 0.0047 ........ 0.0103 ...... 0.0038 ........ 0.0022
5-bit sint #2 ........ 0.0048 ........ 0.0117 ...... 0.0038 ........ 0.0022
5-bit sint #3 ........ 0.0046 ........ 0.0102 ...... 0.0038 ........ 0.0023
8-bit uint #1 ........ 0.0063 ........ 0.0174 ...... 0.0039 ........ 0.0031
8-bit uint #2 ........ 0.0063 ........ 0.0167 ...... 0.0040 ........ 0.0029
8-bit uint #3 ........ 0.0063 ........ 0.0168 ...... 0.0039 ........ 0.0030
16-bit uint #1 ....... 0.0092 ........ 0.0222 ...... 0.0049 ........ 0.0030
16-bit uint #2 ....... 0.0096 ........ 0.0227 ...... 0.0042 ........ 0.0046
16-bit uint #3 ....... 0.0123 ........ 0.0274 ...... 0.0059 ........ 0.0051
32-bit uint #1 ....... 0.0136 ........ 0.0331 ...... 0.0060 ........ 0.0048
32-bit uint #2 ....... 0.0130 ........ 0.0336 ...... 0.0070 ........ 0.0048
32-bit uint #3 ....... 0.0127 ........ 0.0329 ...... 0.0051 ........ 0.0048
64-bit uint #1 ....... 0.0126 ........ 0.0268 ...... 0.0055 ........ 0.0049
64-bit uint #2 ....... 0.0135 ........ 0.0281 ...... 0.0052 ........ 0.0046
64-bit uint #3 ....... 0.0131 ........ 0.0274 ...... 0.0069 ........ 0.0044
8-bit int #1 ......... 0.0077 ........ 0.0236 ...... 0.0058 ........ 0.0044
8-bit int #2 ......... 0.0087 ........ 0.0244 ...... 0.0058 ........ 0.0048
8-bit int #3 ......... 0.0084 ........ 0.0241 ...... 0.0055 ........ 0.0049
16-bit int #1 ........ 0.0112 ........ 0.0271 ...... 0.0048 ........ 0.0045
16-bit int #2 ........ 0.0124 ........ 0.0292 ...... 0.0057 ........ 0.0049
16-bit int #3 ........ 0.0118 ........ 0.0270 ...... 0.0058 ........ 0.0050
32-bit int #1 ........ 0.0137 ........ 0.0366 ...... 0.0058 ........ 0.0051
32-bit int #2 ........ 0.0133 ........ 0.0366 ...... 0.0056 ........ 0.0049
32-bit int #3 ........ 0.0129 ........ 0.0350 ...... 0.0052 ........ 0.0048
64-bit int #1 ........ 0.0145 ........ 0.0254 ...... 0.0034 ........ 0.0025
64-bit int #2 ........ 0.0097 ........ 0.0214 ...... 0.0034 ........ 0.0025
64-bit int #3 ........ 0.0096 ........ 0.0287 ...... 0.0059 ........ 0.0050
64-bit int #4 ........ 0.0143 ........ 0.0277 ...... 0.0059 ........ 0.0046
64-bit float #1 ...... 0.0134 ........ 0.0281 ...... 0.0057 ........ 0.0052
64-bit float #2 ...... 0.0141 ........ 0.0281 ...... 0.0057 ........ 0.0050
64-bit float #3 ...... 0.0144 ........ 0.0282 ...... 0.0057 ........ 0.0050
fix string #1 ........ 0.0036 ........ 0.0143 ...... 0.0066 ........ 0.0053
fix string #2 ........ 0.0107 ........ 0.0222 ...... 0.0065 ........ 0.0068
fix string #3 ........ 0.0116 ........ 0.0245 ...... 0.0063 ........ 0.0069
fix string #4 ........ 0.0105 ........ 0.0253 ...... 0.0083 ........ 0.0077
8-bit string #1 ...... 0.0126 ........ 0.0318 ...... 0.0075 ........ 0.0088
8-bit string #2 ...... 0.0121 ........ 0.0295 ...... 0.0076 ........ 0.0086
8-bit string #3 ...... 0.0125 ........ 0.0293 ...... 0.0130 ........ 0.0093
16-bit string #1 ..... 0.0159 ........ 0.0368 ...... 0.0117 ........ 0.0086
16-bit string #2 ..... 0.1547 ........ 0.1686 ...... 0.1516 ........ 0.1373
32-bit string ........ 0.1558 ........ 0.1729 ...... 0.1511 ........ 0.1396
wide char string #1 .. 0.0098 ........ 0.0237 ...... 0.0066 ........ 0.0065
wide char string #2 .. 0.0128 ........ 0.0291 ...... 0.0061 ........ 0.0082
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0040 ........ 0.0129 ...... 0.0120 ........ 0.0058
fix array #2 ......... 0.0279 ........ 0.0390 ...... 0.0143 ........ 0.0165
fix array #3 ......... 0.0415 ........ 0.0463 ...... 0.0162 ........ 0.0187
16-bit array #1 ...... 0.1349 ........ 0.1628 ...... 0.0334 ........ 0.0341
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0345 ........ 0.0391 ...... 0.0143 ........ 0.0168
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0459 ........ 0.0473 ...... 0.0151 ........ 0.0163
16-bit map #1 ........ 0.2518 ........ 0.2962 ...... 0.0400 ........ 0.0490
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.2380 ........ 0.2682 ...... 0.0545 ........ 0.0579
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  1.5625          2.3866        0.7735          0.7243
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7

With JIT:

MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0001 ........ 0.0052 ...... 0.0053 ........ 0.0042
false ................ 0.0007 ........ 0.0060 ...... 0.0057 ........ 0.0043
true ................. 0.0008 ........ 0.0060 ...... 0.0056 ........ 0.0041
7-bit uint #1 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0041
7-bit uint #2 ........ 0.0021 ........ 0.0043 ...... 0.0062 ........ 0.0041
7-bit uint #3 ........ 0.0022 ........ 0.0044 ...... 0.0061 ........ 0.0040
5-bit sint #1 ........ 0.0030 ........ 0.0048 ...... 0.0062 ........ 0.0040
5-bit sint #2 ........ 0.0032 ........ 0.0046 ...... 0.0062 ........ 0.0040
5-bit sint #3 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0040
8-bit uint #1 ........ 0.0054 ........ 0.0079 ...... 0.0062 ........ 0.0050
8-bit uint #2 ........ 0.0051 ........ 0.0079 ...... 0.0064 ........ 0.0044
8-bit uint #3 ........ 0.0051 ........ 0.0082 ...... 0.0062 ........ 0.0044
16-bit uint #1 ....... 0.0077 ........ 0.0094 ...... 0.0065 ........ 0.0045
16-bit uint #2 ....... 0.0077 ........ 0.0094 ...... 0.0063 ........ 0.0045
16-bit uint #3 ....... 0.0077 ........ 0.0095 ...... 0.0064 ........ 0.0047
32-bit uint #1 ....... 0.0088 ........ 0.0119 ...... 0.0063 ........ 0.0043
32-bit uint #2 ....... 0.0089 ........ 0.0117 ...... 0.0062 ........ 0.0039
32-bit uint #3 ....... 0.0089 ........ 0.0118 ...... 0.0063 ........ 0.0044
64-bit uint #1 ....... 0.0097 ........ 0.0155 ...... 0.0063 ........ 0.0045
64-bit uint #2 ....... 0.0095 ........ 0.0153 ...... 0.0061 ........ 0.0045
64-bit uint #3 ....... 0.0096 ........ 0.0156 ...... 0.0063 ........ 0.0047
8-bit int #1 ......... 0.0053 ........ 0.0083 ...... 0.0062 ........ 0.0044
8-bit int #2 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0044
8-bit int #3 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0043
16-bit int #1 ........ 0.0089 ........ 0.0097 ...... 0.0069 ........ 0.0046
16-bit int #2 ........ 0.0075 ........ 0.0093 ...... 0.0063 ........ 0.0043
16-bit int #3 ........ 0.0075 ........ 0.0094 ...... 0.0062 ........ 0.0046
32-bit int #1 ........ 0.0086 ........ 0.0122 ...... 0.0063 ........ 0.0044
32-bit int #2 ........ 0.0087 ........ 0.0120 ...... 0.0066 ........ 0.0046
32-bit int #3 ........ 0.0086 ........ 0.0121 ...... 0.0060 ........ 0.0044
64-bit int #1 ........ 0.0096 ........ 0.0149 ...... 0.0060 ........ 0.0045
64-bit int #2 ........ 0.0096 ........ 0.0157 ...... 0.0062 ........ 0.0044
64-bit int #3 ........ 0.0096 ........ 0.0160 ...... 0.0063 ........ 0.0046
64-bit int #4 ........ 0.0097 ........ 0.0157 ...... 0.0061 ........ 0.0044
64-bit float #1 ...... 0.0079 ........ 0.0153 ...... 0.0056 ........ 0.0044
64-bit float #2 ...... 0.0079 ........ 0.0152 ...... 0.0057 ........ 0.0045
64-bit float #3 ...... 0.0079 ........ 0.0155 ...... 0.0057 ........ 0.0044
fix string #1 ........ 0.0010 ........ 0.0045 ...... 0.0071 ........ 0.0044
fix string #2 ........ 0.0048 ........ 0.0075 ...... 0.0070 ........ 0.0060
fix string #3 ........ 0.0048 ........ 0.0086 ...... 0.0068 ........ 0.0060
fix string #4 ........ 0.0050 ........ 0.0088 ...... 0.0070 ........ 0.0059
8-bit string #1 ...... 0.0081 ........ 0.0129 ...... 0.0069 ........ 0.0062
8-bit string #2 ...... 0.0086 ........ 0.0128 ...... 0.0069 ........ 0.0065
8-bit string #3 ...... 0.0086 ........ 0.0126 ...... 0.0115 ........ 0.0065
16-bit string #1 ..... 0.0105 ........ 0.0137 ...... 0.0128 ........ 0.0068
16-bit string #2 ..... 0.1510 ........ 0.1486 ...... 0.1526 ........ 0.1391
32-bit string ........ 0.1517 ........ 0.1475 ...... 0.1504 ........ 0.1370
wide char string #1 .. 0.0044 ........ 0.0085 ...... 0.0067 ........ 0.0057
wide char string #2 .. 0.0081 ........ 0.0125 ...... 0.0069 ........ 0.0063
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0014 ........ 0.0059 ...... 0.0132 ........ 0.0055
fix array #2 ......... 0.0146 ........ 0.0156 ...... 0.0155 ........ 0.0148
fix array #3 ......... 0.0211 ........ 0.0229 ...... 0.0179 ........ 0.0180
16-bit array #1 ...... 0.0673 ........ 0.0498 ...... 0.0343 ........ 0.0388
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0148 ........ 0.0180 ...... 0.0156 ........ 0.0179
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0252 ........ 0.0201 ...... 0.0214 ........ 0.0167
16-bit map #1 ........ 0.1027 ........ 0.0836 ...... 0.0388 ........ 0.0510
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.1104 ........ 0.1010 ...... 0.0556 ........ 0.0602
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  0.9642          1.0909        0.8224          0.7213
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7

Note that the msgpack extension (v2.1.2) doesn't support ext, bin and UTF-8 str types.

License

The library is released under the MIT License. See the bundled LICENSE file for details.

Author: rybakit
Source Code: https://github.com/rybakit/msgpack.php
License: MIT License

#php 

Dijkstra's Algorithm Explained with Examples

In this tutorial, you'll learn: What is Dijkstra's Algorithm and how Dijkstra's algorithm works with the help of visual guides.

You can use algorithms in programming to solve specific problems through a set of precise instructions or procedures.

Dijkstra's algorithm is one of many graph algorithms you'll come across. It is used to find the shortest path from a fixed node to all other nodes in a graph.

There are different representations of Dijkstra's algorithm. You can either find the shortest path between two nodes, or the shortest path from a fixed node to the rest of the nodes in a graph.

In this article, you'll learn how Dijkstra's algorithm works with the help of visual guides.

How Does Dijkstra’s Algorithm Work?

Before we dive into more detailed visual examples, you need to understand how Dijkstra's algorithm works.

Although the theoretical explanation may seem a bit abstract, it'll help you understand the practical aspect better.

In a given graph containing different nodes, we are required to get the shortest path from a given node to the rest of the nodes.

These nodes can represent any object like the names of cities, letters, and so on.

Between each node is a number denoting the distance between two nodes, as you can see in the image below:

We usually work with two arrays – one for visited nodes, and another for unvisited nodes. You'll learn more about the arrays in the next section.

When a node is visited, the algorithm calculates how long it took to get to the node and stores the distance. If a shorter path to a node is found, the initial value assigned for the distance is updated.

Note that a node cannot be visited twice.

The algorithm runs recursively until all the nodes have been visited.

Dijkstra's Algorithm Example

In this section, we'll take a look at a practical example that shows how Dijkstra's algorithm works.

Here's the graph we'll be working with:

We'll use the table below to put down the visited nodes and their distance from the fixed node:

NODE	SHORTEST DISTANCE FROM FIXED NODE
A	∞
B	∞
C	∞
D	∞
E	∞

Visited nodes = []
Unvisited nodes = [A,B,C,D,E]

Above, we have a table showing each node and the shortest distance from the that node to the fixed node. We are yet to choose the fixed node.

Note that the distance for each node in the table is currently denoted as infinity (∞). This is because we don't know the shortest distance yet.

We also have two arrays – visited and unvisited. Whenever a node is visited, it is added to the visited nodes array.

Let's get started!

To simplify things, I'll break the process down into iterations. You'll see what happens in each step with the aid of diagrams.

Iteration #1

The first iteration might seem confusing, but that's totally fine. Once we start repeating the process in each iteration, you'll have a clearer picture of how the algorithm works.

Step #1 - Pick an unvisited node

We'll choose A as the fixed node. So we'll find the shortest distance from A to every other node in the graph.

We're going to give A a distance of 0 because it is the initial node. So the table would look like this:

NODE	SHORTEST DISTANCE FROM FIXED NODE
A	0
B	∞
C	∞
D	∞
E	∞

Step #2 - Find the distance from current node

The next thing to do after choosing a node is to find the distance from it to the unvisited nodes around it.

The two unvisited nodes directly linked to A are B and C.

To get the distance from A to B:

0 + 4 = 4

0 being the value of the current node (A), and 4 being the distance between A and B in the graph.

To get the distance from A to C:

0 + 2 = 2

Step #3 - Update table with known distances

In the last step, we got 4 and 2 as the values of B and C respectively. So we'll update the table with those values:

NODE	SHORTEST DISTANCE FROM FIXED NODE
A	0
B	4
C	2
D	∞
E	∞

Step #4 - Update arrays

At this point, the first iteration is complete. We'll move node A to the visited nodes array:

Visited nodes = [A]
Unvisited nodes = [B,C,D,E]

Before we proceed to the next iteration, you should know the following:

• Once a node has been visited, it cannot be linked to the current node. Refer to step #2 in the iteration above and step #2 in the next iteration.
• A node cannot be visited twice.
• You can only update the shortest known distance if you get a value smaller than the recorded distance.

Iteration #2

Step #1 - Pick an unvisited node

We have four unvisited nodes — [B,C,D,E]. So how do you know which node to pick for the next iteration?

Well, we pick the node with the smallest known distance recorded in the table. Here's the table:

NODE	SHORTEST DISTANCE FROM FIXED NODE
A	0
B	4
C	2
D	∞
E	∞

So we're going with node C.

Step #2 - Find the distance from current node

To find the distance from the current node to the fixed node, we have to consider the nodes linked to the current node.

The nodes linked to the current node are A and B.

But A has been visited in the previous iteration so it will not be linked to the current node. That is:

From the diagram above,

• The green color denotes the current node.
• The blue color denotes the visited nodes. We cannot link to them or visit them again.
• The red color shows the link from the unvisited nodes to the current node.

To find the distance from C to B:

2 + 1 = 3

2 above is recorded distance for node C while 1 is the distance between C and B in the graph.

Step #3 - Update table with known distances

In the last step, we got the value of B to be 3. In the first iteration, it was 4.

We're going to update the distance in the table to 3.

NODE	SHORTEST DISTANCE FROM FIXED NODE
A	0
B	3
C	2
D	∞
E	∞

So, A --> B = 4 (First iteration).

A --> C --> B = 3 (Second iteration).

The algorithm has helped us find the shortest path to B from A.

Step #4 - Update arrays

We're done with the last visited node. Let's add it to the visited nodes array:

Visited nodes = [A,C]
Unvisited nodes = [B,D,E]

Iteration #3

Step #1 - Pick an unvisited node

We're down to three unvisited nodes — [B,D,E]. From the array, B has the shortest known distance.

To restate what is going on in the diagram above:

• The green color denotes the current node.
• The blue color denotes the visited nodes. We cannot link to them or visit them again.
• The red color shows the link from the unvisited nodes to the current node.

Step #2 - Find the distance from current node

The nodes linked to the current node are D and E.

B (the current node) has a value of 3. Therefore,

For node D, 3 + 3 = 6.

For node E, 3 + 2 = 5.

Step #3 - Update table with known distances

NODE	SHORTEST DISTANCE FROM FIXED NODE
A	0
B	3
C	2
D	6
E	5

Step #4 - Update arrays

Visited nodes = [A,C,B]
Unvisited nodes = [D,E]

Iteration #4

Step #1 - Pick an unvisited node

Like other iterations, we'll go with the unvisited node with the shortest known distance. That is E.

Step #2 - Find the distance from current node

According to our table, E has a value of 5.

For D in the current iteration,

5 + 5 = 10.

The value gotten for D here is 10, which is greater than the recorded value of 6 in the previous iteration. For this reason, we'll not update the table.

Step #3 - Update table with known distances

Our table remains the same:

NODE	SHORTEST DISTANCE FROM FIXED NODE
A	0
B	3
C	2
D	6
E	5

Step #4 - Update arrays

Visited nodes = [A,C,B,E]
Unvisited nodes = [D]

Iteration #5

Step #1 - Pick an unvisited node

We're currently left with one node in the unvisited array — D.

Step #2 - Find the distance from current node

The algorithm has gotten to the last iteration. This is because all nodes linked to the current node have been visited already so we can't link to them.

Step #3 - Update table with known distances

Our table remains the same:

NODE	SHORTEST DISTANCE FROM FIXED NODE
A	0
B	3
C	2
D	6
E	5

At this point, we have updated the table with the shortest distance from the fixed node to every other node in the graph.

Step #4 - Update arrays

Visited nodes = [A,C,B,E,D]
Unvisited nodes = []

As can be seen above, we have no nodes left to visit. Using Dijkstra's algorithm, we've found the shortest distance from the fixed node to others nodes in the graph.

Dijkstra's Algorithm Pseudocode Example

The pseudocode example in this section was gotten from Wikipedia. Here it is:

 1  function Dijkstra(Graph, source):
 2      
 3      for each vertex v in Graph.Vertices:
 4          dist[v] ← INFINITY
 5          prev[v] ← UNDEFINED
 6          add v to Q
 7      dist[source] ← 0
 8      
 9      while Q is not empty:
10          u ← vertex in Q with min dist[u]
11          remove u from Q
12          
13          for each neighbor v of u still in Q:
14              alt ← dist[u] + Graph.Edges(u, v)
15              if alt < dist[v]:
16                  dist[v] ← alt
17                  prev[v] ← u
18
19      return dist[], prev[]

Applications of Dijkstra's Algorithm

Here are some of the common applications of Dijkstra's algorithm:

• In maps to get the shortest distance between locations. An example is Google Maps.
• In telecommunications to determine transmission rate.
• In robotic design to determine shortest path for automated robots.

Summary

In this article, we talked about Dijkstra's algorithm. It is used to find the shortest distance from a fixed node to all other nodes in a graph.

We started by giving a brief summary of how the algorithm works.

We then had a look at an example that further explained Dijkstra's algorithm in steps using visual guides.

We concluded with a pseudocode example and some of the applications of Dijkstra's algorithm.

Happy coding!

Original article source at https://www.freecodecamp.org

#algorithm #datastructures

파이썬 코딩 무료 강의 - 이미지 처리, 얼굴 인식을 통한 캐릭터 씌우기를 해보아요

파이썬 코딩 무료 강의 (활용편6) - 이미지 처리, 얼굴 인식을 통한 캐릭터 씌우기를 해보아요

파이썬 무료 강의 (활용편6 - 이미지 처리)입니다.
OpenCV 를 이용한 다양한 이미지 처리 기법과 재미있는 프로젝트를 진행합니다.
누구나 볼 수 있도록 쉽고 재미있게 제작하였습니다. ^^

[소개]
(0:00:00) 0.Intro
(0:00:31) 1.소개
(0:02:18) 2.활용편 6 이미지 처리 소개

[OpenCV 전반전]
(0:04:36) 3.환경설정
(0:08:41) 4.이미지 출력
(0:21:51) 5.동영상 출력 #1 파일
(0:29:58) 6.동영상 출력 #2 카메라
(0:34:23) 7.도형 그리기 #1 빈 스케치북
(0:39:49) 8.도형 그리기 #2 영역 색칠
(0:42:26) 9.도형 그리기 #3 직선
(0:51:23) 10.도형 그리기 #4 원
(0:55:09) 11.도형 그리기 #5 사각형
(0:58:32) 12.도형 그리기 #6 다각형
(1:09:23) 13.텍스트 #1 기본
(1:17:45) 14.텍스트 #2 한글 우회
(1:24:14) 15.파일 저장 #1 이미지
(1:29:27) 16.파일 저장 #2 동영상
(1:39:29) 17.크기 조정
(1:50:16) 18.이미지 자르기
(1:57:03) 19.이미지 대칭
(2:01:46) 20.이미지 회전
(2:06:07) 21.이미지 변형 - 흑백
(2:11:25) 22.이미지 변형 - 흐림
(2:18:03) 23.이미지 변형 - 원근 #1
(2:27:45) 24.이미지 변형 - 원근 #2

[반자동 문서 스캐너 프로젝트]
(2:32:50) 25.미니 프로젝트 1 - #1 마우스 이벤트 등록
(2:42:06) 26.미니 프로젝트 1 - #2 기본 코드 완성
(2:49:54) 27.미니 프로젝트 1 - #3 지점 선 긋기
(2:55:24) 28.미니 프로젝트 1 - #4 실시간 선 긋기

[OpenCV 후반전]
(3:01:52) 29.이미지 변형 - 이진화 #1 Trackbar
(3:14:37) 30.이미지 변형 - 이진화 #2 임계값
(3:20:26) 31.이미지 변형 - 이진화 #3 Adaptive Threshold
(3:28:34) 32.이미지 변형 - 이진화 #4 오츠 알고리즘
(3:32:22) 33.이미지 변환 - 팽창
(3:41:10) 34.이미지 변환 - 침식
(3:45:56) 35.이미지 변환 - 열림 & 닫힘
(3:54:10) 36.이미지 검출 - 경계선
(4:05:08) 37.이미지 검출 - 윤곽선 #1 기본
(4:15:26) 38.이미지 검출 - 윤곽선 #2 찾기 모드
(4:20:46) 39.이미지 검출 - 윤곽선 #3 면적

[카드 검출 & 분류기 프로젝트]
(4:27:42) 40.미니프로젝트 2

[퀴즈]
(4:31:57) 41.퀴즈

[얼굴인식 프로젝트]
(4:41:25) 42.환경설정 및 기본 코드 정리
(4:54:48) 43.눈과 코 인식하여 도형 그리기
(5:10:42) 44.그림판 이미지 씌우기
(5:20:52) 45.캐릭터 이미지 씌우기
(5:33:10) 46.보충설명
(5:40:53) 47.마치며 (학습 참고 자료)
(5:42:18) 48.Outro

[학습자료]
수업에 필요한 이미지, 동영상 자료 링크입니다.

고양이 이미지 : https://pixabay.com/images/id-2083492/ 
크기 : 640 x 390  
파일명 : img.jpg

고양이 동영상 : https://www.pexels.com/video/7515833/ 
크기 : SD (360 x 640)  
파일명 : video.mp4

신문 이미지 : https://pixabay.com/images/id-350376/ 
크기 : 1280 x 853  
파일명 : newspaper.jpg

카드 이미지 1 : https://pixabay.com/images/id-682332/ 
크기 : 1280 x 1019  
파일명 : poker.jpg

책 이미지 : https://www.pexels.com/photo/1029807/ 
크기 : Small (640 x 853)  
파일명 : book.jpg

눈사람 이미지 : https://pixabay.com/images/id-1300089/ 
크기 : 1280 x 904  
파일명 : snowman.png

카드 이미지 2 : https://pixabay.com/images/id-161404/ 
크기 : 640 x 408  
파일명 : card.png

퀴즈용 동영상 : https://www.pexels.com/video/3121459/ 
크기 : HD (1280 x 720)  
파일명 : city.mp4

프로젝트용 동영상 : https://www.pexels.com/video/3256542/ 
크기 : Full HD (1920 x 1080)  
파일명 : face_video.mp4

프로젝트용 캐릭터 이미지 : https://www.freepik.com/free-vector/cute-animal-masks-video-chat-application-effect-filters-set_6380101.htm  
파일명 : right_eye.png (100 x 100), left_eye.png (100 x 100), nose.png (300 x 100)

무료 이미지 편집 도구 : https://pixlr.com/kr/
(Pixlr E -Advanced Editor)

#python #opencv 

Chatgpt-api: Node.js client for the official ChatGPT API

ChatGPT API

Node.js client for the official ChatGPT API.

Intro

This package is a Node.js wrapper around ChatGPT by OpenAI. TS batteries included. ✨

Updates

March 1, 2023

The official OpenAI chat completions API has been released, and it is now the default for this package! 🔥

Method	Free?	Robust?	Quality?
ChatGPTAPI	❌ No	✅ Yes	✅️ Real ChatGPT models
ChatGPTUnofficialProxyAPI	✅ Yes	☑️ Maybe	✅ Real ChatGPT

Note: We strongly recommend using ChatGPTAPI since it uses the officially supported API from OpenAI. We may remove support for ChatGPTUnofficialProxyAPI in a future release.

1. ChatGPTAPI - Uses the gpt-3.5-turbo-0301 model with the official OpenAI chat completions API (official, robust approach, but it's not free)
2. ChatGPTUnofficialProxyAPI - Uses an unofficial proxy server to access ChatGPT's backend API in a way that circumvents Cloudflare (uses the real ChatGPT and is pretty lightweight, but relies on a third-party server and is rate-limited)

CLI

To run the CLI, you'll need an OpenAI API key:

export OPENAI_API_KEY="sk-TODO"
npx chatgpt "your prompt here"

By default, the response is streamed to stdout, the results are stored in a local config file, and every invocation starts a new conversation. You can use -c to continue the previous conversation and --no-stream to disable streaming.

Under the hood, the CLI uses ChatGPTAPI with text-davinci-003 to mimic ChatGPT.

Usage:
  $ chatgpt <prompt>

Commands:
  <prompt>  Ask ChatGPT a question
  rm-cache  Clears the local message cache
  ls-cache  Prints the local message cache path

For more info, run any command with the `--help` flag:
  $ chatgpt --help
  $ chatgpt rm-cache --help
  $ chatgpt ls-cache --help

Options:
  -c, --continue          Continue last conversation (default: false)
  -d, --debug             Enables debug logging (default: false)
  -s, --stream            Streams the response (default: true)
  -s, --store             Enables the local message cache (default: true)
  -t, --timeout           Timeout in milliseconds
  -k, --apiKey            OpenAI API key
  -n, --conversationName  Unique name for the conversation
  -h, --help              Display this message
  -v, --version           Display version number

Install

npm install chatgpt

Make sure you're using node >= 18 so fetch is available (or node >= 14 if you install a fetch polyfill).

Usage

To use this module from Node.js, you need to pick between two methods:

Method	Free?	Robust?	Quality?
ChatGPTAPI	❌ No	✅ Yes	✅️ Real ChatGPT models
ChatGPTUnofficialProxyAPI	✅ Yes	☑️ Maybe	✅ Real ChatGPT

ChatGPTAPI - Uses the gpt-3.5-turbo-0301 model with the official OpenAI chat completions API (official, robust approach, but it's not free). You can override the model, completion params, and system message to fully customize your assistant.

ChatGPTUnofficialProxyAPI - Uses an unofficial proxy server to access ChatGPT's backend API in a way that circumvents Cloudflare (uses the real ChatGPT and is pretty lightweight, but relies on a third-party server and is rate-limited)

Both approaches have very similar APIs, so it should be simple to swap between them.

Note: We strongly recommend using ChatGPTAPI since it uses the officially supported API from OpenAI. We may remove support for ChatGPTUnofficialProxyAPI in a future release.

Usage - ChatGPTAPI

Sign up for an OpenAI API key and store it in your environment.

import { ChatGPTAPI } from 'chatgpt'

async function example() {
  const api = new ChatGPTAPI({
    apiKey: process.env.OPENAI_API_KEY
  })

  const res = await api.sendMessage('Hello World!')
  console.log(res.text)
}

You can override the default model (gpt-3.5-turbo-0301) and any OpenAI chat completion params using completionParams:

const api = new ChatGPTAPI({
  apiKey: process.env.OPENAI_API_KEY,
  completionParams: {
    temperature: 0.5,
    top_p: 0.8
  }
})

If you want to track the conversation, you'll need to pass the parentMessageId like this:

const api = new ChatGPTAPI({ apiKey: process.env.OPENAI_API_KEY })

// send a message and wait for the response
let res = await api.sendMessage('What is OpenAI?')
console.log(res.text)

// send a follow-up
res = await api.sendMessage('Can you expand on that?', {
  parentMessageId: res.id
})
console.log(res.text)

// send another follow-up
res = await api.sendMessage('What were we talking about?', {
  parentMessageId: res.id
})
console.log(res.text)

You can add streaming via the onProgress handler:

const res = await api.sendMessage('Write a 500 word essay on frogs.', {
  // print the partial response as the AI is "typing"
  onProgress: (partialResponse) => console.log(partialResponse.text)
})

// print the full text at the end
console.log(res.text)

You can add a timeout using the timeoutMs option:

// timeout after 2 minutes (which will also abort the underlying HTTP request)
const response = await api.sendMessage(
  'write me a really really long essay on frogs',
  {
    timeoutMs: 2 * 60 * 1000
  }
)

If you want to see more info about what's actually being sent to OpenAI's chat completions API, set the debug: true option in the ChatGPTAPI constructor:

const api = new ChatGPTAPI({
  apiKey: process.env.OPENAI_API_KEY,
  debug: true
})

We default to a basic systemMessage. You can override this in either the ChatGPTAPI constructor or sendMessage:

const res = await api.sendMessage('what is the answer to the universe?', {
  systemMessage: `You are ChatGPT, a large language model trained by OpenAI. You answer as concisely as possible for each responseIf you are generating a list, do not have too many items.
Current date: ${new Date().toISOString()}\n\n`
})

Note that we automatically handle appending the previous messages to the prompt and attempt to optimize for the available tokens (which defaults to 4096).

Usage in CommonJS (Dynamic import)

async function example() {
  // To use ESM in CommonJS, you can use a dynamic import
  const { ChatGPTAPI } = await import('chatgpt')

  const api = new ChatGPTAPI({ apiKey: process.env.OPENAI_API_KEY })

  const res = await api.sendMessage('Hello World!')
  console.log(res.text)
}

Usage - ChatGPTUnofficialProxyAPI

The API for ChatGPTUnofficialProxyAPI is almost exactly the same. You just need to provide a ChatGPT accessToken instead of an OpenAI API key.

import { ChatGPTUnofficialProxyAPI } from 'chatgpt'

async function example() {
  const api = new ChatGPTUnofficialProxyAPI({
    accessToken: process.env.OPENAI_ACCESS_TOKEN
  })

  const res = await api.sendMessage('Hello World!')
  console.log(res.text)
}

See demos/demo-reverse-proxy for a full example:

npx tsx demos/demo-reverse-proxy.ts

ChatGPTUnofficialProxyAPI messages also contain a conversationid in addition to parentMessageId, since the ChatGPT webapp can't reference messages across

Reverse Proxy

You can override the reverse proxy by passing apiReverseProxyUrl:

const api = new ChatGPTUnofficialProxyAPI({
  accessToken: process.env.OPENAI_ACCESS_TOKEN,
  apiReverseProxyUrl: 'https://your-example-server.com/api/conversation'
})

Known reverse proxies run by community members include:

Reverse Proxy URL	Author	Rate Limits	Last Checked
https://chat.duti.tech/api/conversation	@acheong08	120 req/min by IP	2/19/2023
https://gpt.pawan.krd/backend-api/conversation	@PawanOsman	?	2/19/2023

Note: info on how the reverse proxies work is not being published at this time in order to prevent OpenAI from disabling access.

Access Token

To use ChatGPTUnofficialProxyAPI, you'll need an OpenAI access token from the ChatGPT webapp. To do this, you can use any of the following methods which take an email and password and return an access token:

• Node.js libs
  • ericlewis/openai-authenticator
  • michael-dm/openai-token
  • allanoricil/chat-gpt-authenticator
• Python libs
  • acheong08/OpenAIAuth

These libraries work with email + password accounts (e.g., they do not support accounts where you auth via Microsoft / Google).

Alternatively, you can manually get an accessToken by logging in to the ChatGPT webapp and then opening https://chat.openai.com/api/auth/session, which will return a JSON object containing your accessToken string.

Access tokens last for days.

Note: using a reverse proxy will expose your access token to a third-party. There shouldn't be any adverse effects possible from this, but please consider the risks before using this method.

Docs

See the auto-generated docs for more info on methods and parameters.

Demos

Most of the demos use ChatGPTAPI. It should be pretty easy to convert them to use ChatGPTUnofficialProxyAPI if you'd rather use that approach. The only thing that needs to change is how you initialize the api with an accessToken instead of an apiKey.

To run the included demos:

1. clone repo
2. install node deps
3. set OPENAI_API_KEY in .env

A basic demo is included for testing purposes:

npx tsx demos/demo.ts

A demo showing on progress handler:

npx tsx demos/demo-on-progress.ts

The on progress demo uses the optional onProgress parameter to sendMessage to receive intermediary results as ChatGPT is "typing".

A conversation demo:

npx tsx demos/demo-conversation.ts

A persistence demo shows how to store messages in Redis for persistence:

npx tsx demos/demo-persistence.ts

Any keyv adaptor is supported for persistence, and there are overrides if you'd like to use a different way of storing / retrieving messages.

Note that persisting message is required for remembering the context of previous conversations beyond the scope of the current Node.js process, since by default, we only store messages in memory. Here's an external demo of using a completely custom database solution to persist messages.

Note: Persistence is handled automatically when using ChatGPTUnofficialProxyAPI because it is connecting indirectly to ChatGPT.

Projects

All of these awesome projects are built using the chatgpt package. 🤯

• Twitter Bot powered by ChatGPT ✨
  • Mention @ChatGPTBot on Twitter with your prompt to try it out
• ChatGPT API Server - API server for this package with support for multiple OpenAI accounts, proxies, and load-balancing requests between accounts.
• ChatGPT Prompts - A collection of 140+ of the best ChatGPT prompts from the community.
• Lovelines.xyz
• Chrome Extension (demo)
• VSCode Extension #1 (demo, updated version, marketplace)
• VSCode Extension #2 (marketplace)
• VSCode Extension #3 (marketplace)
• VSCode Extension #4 (marketplace)
• Raycast Extension #1 (demo)
• Raycast Extension #2
• Telegram Bot #1
• Telegram Bot #2
• Telegram Bot #3 (group privacy mode, ID-based auth)
• Telegram Bot #4 (queue system, ID-based chat thread)
• Deno Telegram Bot
• Go Telegram Bot
• Telegram Bot for YouTube Summaries
• GitHub ProBot
• Discord Bot #1
• Discord Bot #2
• Discord Bot #3
• Discord Bot #4 (selfbot)
• Discord Bot #5
• Discord Bot #6 (Shakespeare bot)
• WeChat Bot #1
• WeChat Bot #2
• WeChat Bot #3 (
• WeChat Bot #4
• WeChat Bot #5
• QQ Bot (plugin for Yunzai-bot)
• QQ Bot (plugin for KiviBot)
• QQ Bot (oicq)
• QQ Bot (oicq + RabbitMQ)
• QQ Bot (go-cqhttp)
• EXM smart contracts
• Flutter ChatGPT API
• Carik Bot
• Github Action for reviewing PRs
• WhatsApp Bot #1 (DALL-E + Whisper support 💪)
• WhatsApp Bot #2
• WhatsApp Bot #3 (multi-user support)
• WhatsApp Bot #4 (schedule periodic messages)
• WhatsApp Bot #5 (RaspberryPi + ngrok + Twilio)
• WhatsApp Bot #6 (Session and chat history storage with MongoStore)
• Matrix Bot
• Rental Cover Letter Generator
• Assistant CLI
• Teams Bot
• Askai
• TalkGPT
• ChatGPT With Voice
• iOS Shortcut
• Slack Bot #1
• Slack Bot #2 (with queueing mechanism)
• Slack Bot #3
• Slack Bot #4 ( Serverless AWS Lambda )
• Electron Bot
• Kodyfire CLI
• Twitch Bot
• Continuous Conversation
• Figma plugin
• NestJS server
• NestJS ChatGPT Starter Boilerplate
• Wordsmith: Add-in for Microsoft Word
• QuizGPT: Create Kahoot quizzes with ChatGPT
• openai-chatgpt: Talk to ChatGPT from the terminal
• Clippy the Saleforce chatbot ClippyJS joke bot
• ai-assistant Chat assistant
• Feishu Bot
• DomainGPT: Discover available domain names
• AI Poem Generator
• Next.js ChatGPT With Firebase
• ai-commit – GPT-3 Commit Message Generator

If you create a cool integration, feel free to open a PR and add it to the list.

Compatibility

• This package is ESM-only.
• This package supports node >= 14.
• This module assumes that fetch is installed.
  • In node >= 18, it's installed by default.
  • In node < 18, you need to install a polyfill like unfetch/polyfill (guide) or isomorphic-fetch (guide).
• If you want to build a website using chatgpt, we recommend using it only from your backend API

Credits

• Huge thanks to @waylaidwanderer, @abacaj, @wong2, @simon300000, @RomanHotsiy, @ElijahPepe, and all the other contributors 💪
• The original browser version was inspired by this Go module by Daniel Gross
• OpenAI for creating ChatGPT 🔥

---

Previous Updates

Feb 19, 2023
 

We now provide three ways of accessing the unofficial ChatGPT API, all of which have tradeoffs:

Method	Free?	Robust?	Quality?
ChatGPTAPI	❌ No	✅ Yes	☑️ Mimics ChatGPT
ChatGPTUnofficialProxyAPI	✅ Yes	☑️ Maybe	✅ Real ChatGPT
ChatGPTAPIBrowser (v3)	✅ Yes	❌ No	✅ Real ChatGPT

Note: I recommend that you use either ChatGPTAPI or ChatGPTUnofficialProxyAPI.

1. ChatGPTAPI - Uses text-davinci-003 to mimic ChatGPT via the official OpenAI completions API (most robust approach, but it's not free and doesn't use a model fine-tuned for chat)
2. ChatGPTUnofficialProxyAPI - Uses an unofficial proxy server to access ChatGPT's backend API in a way that circumvents Cloudflare (uses the real ChatGPT and is pretty lightweight, but relies on a third-party server and is rate-limited)
3. ChatGPTAPIBrowser - (deprecated; v3.5.1 of this package) Uses Puppeteer to access the official ChatGPT webapp (uses the real ChatGPT, but very flaky, heavyweight, and error prone)

Feb 5, 2023
 

OpenAI has disabled the leaked chat model we were previously using, so we're now defaulting to text-davinci-003, which is not free.

We've found several other hidden, fine-tuned chat models, but OpenAI keeps disabling them, so we're searching for alternative workarounds.

Feb 1, 2023
 

This package no longer requires any browser hacks – it is now using the official OpenAI completions API with a leaked model that ChatGPT uses under the hood. 🔥

import { ChatGPTAPI } from 'chatgpt'

const api = new ChatGPTAPI({
  apiKey: process.env.OPENAI_API_KEY
})

const res = await api.sendMessage('Hello World!')
console.log(res.text)

Please upgrade to chatgpt@latest (at least v4.0.0). The updated version is significantly more lightweight and robust compared with previous versions. You also don't have to worry about IP issues or rate limiting.

Huge shoutout to @waylaidwanderer for discovering the leaked chat model!

If you run into any issues, we do have a pretty active Discord with a bunch of ChatGPT hackers from the Node.js & Python communities.

Lastly, please consider starring this repo and following me on twitter to help support the project.

Thanks && cheers, Travis

---

Download Details:

Author: Transitive-bullshit
Source Code: https://github.com/transitive-bullshit/chatgpt-api 
License: MIT license

#chatgpt #api #node #AI #openai #chatbot 

Treebender: A Symbolic Natural Language Parsing Library for Rust

Treebender

A symbolic natural language parsing library for Rust, inspired by HDPSG.

What is this?

This is a library for parsing natural or constructed languages into syntax trees and feature structures. There's no machine learning or probabilistic models, everything is hand-crafted and deterministic.

You can find out more about the motivations of this project in this blog post.

But what are you using it for?

I'm using this to parse a constructed language for my upcoming xenolinguistics game, Themengi.

Motivation

Using a simple 80-line grammar, introduced in the tutorial below, we can parse a simple subset of English, checking reflexive pronoun binding, case, and number agreement.

$ cargo run --bin cli examples/reflexives.fgr
> she likes himself
Parsed 0 trees

> her likes herself
Parsed 0 trees

> she like herself
Parsed 0 trees

> she likes herself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: she))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: herself)))
[
  child-2: [
    case: acc
    pron: ref
    needs_pron: #0 she
    num: sg
    child-0: [ word: herself ]
  ]
  child-1: [
    tense: nonpast
    child-0: [ word: likes ]
    num: #1 sg
  ]
  child-0: [
    child-0: [ word: she ]
    case: nom
    pron: #0
    num: #1
  ]
]

Low resource language? Low problem! No need to train on gigabytes of text, just write a grammar using your brain. Let's hypothesize that in American Sign Language, topicalized nouns (expressed with raised eyebrows) must appear first in the sentence. We can write a small grammar (18 lines), and plug in some sentences:

$ cargo run --bin cli examples/asl-wordorder.fgr -n
> boy sit
Parsed 1 tree
(0..2: S
  (0..1: NP ((0..1: N (0..1: boy))))
  (1..2: IV (1..2: sit)))

> boy throw ball
Parsed 1 tree
(0..3: S
  (0..1: NP ((0..1: N (0..1: boy))))
  (1..2: TV (1..2: throw))
  (2..3: NP ((2..3: N (2..3: ball)))))

> ball nm-raised-eyebrows boy throw
Parsed 1 tree
(0..4: S
  (0..2: NP
    (0..1: N (0..1: ball))
    (1..2: Topic (1..2: nm-raised-eyebrows)))
  (2..3: NP ((2..3: N (2..3: boy))))
  (3..4: TV (3..4: throw)))

> boy throw ball nm-raised-eyebrows
Parsed 0 trees

Tutorial

As an example, let's say we want to build a parser for English reflexive pronouns (himself, herself, themselves, themself, itself). We'll also support number ("He likes X" v.s. "They like X") and simple embedded clauses ("He said that they like X").

Grammar files are written in a custom language, similar to BNF, called Feature GRammar (.fgr). There's a VSCode syntax highlighting extension for these files available as fgr-syntax.

We'll start by defining our lexicon. The lexicon is the set of terminal symbols (symbols in the actual input) that the grammar will match. Terminal symbols must start with a lowercase letter, and non-terminal symbols must start with an uppercase letter.

// pronouns
N -> he
N -> him
N -> himself
N -> she
N -> her
N -> herself
N -> they
N -> them
N -> themselves
N -> themself

// names, lowercase as they are terminals
N -> mary
N -> sue
N -> takeshi
N -> robert

// complementizer
Comp -> that

// verbs -- intransitive, transitive, and clausal
IV -> falls
IV -> fall
IV -> fell

TV -> likes
TV -> like
TV -> liked

CV -> says
CV -> say
CV -> said

Next, we can add our sentence rules (they must be added at the top, as the first rule in the file is assumed to be the top-level rule):

// sentence rules
S -> N IV
S -> N TV N
S -> N CV Comp S

// ... previous lexicon ...

Assuming this file is saved as examples/no-features.fgr (which it is :wink:), we can test this file with the built-in CLI:

$ cargo run --bin cli examples/no-features.fgr
> he falls
Parsed 1 tree
(0..2: S
  (0..1: N (0..1: he))
  (1..2: IV (1..2: falls)))
[
  child-1: [ child-0: [ word: falls ] ]
  child-0: [ child-0: [ word: he ] ]
]

> he falls her
Parsed 0 trees

> he likes her
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: he))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: her)))
[
  child-2: [ child-0: [ word: her ] ]
  child-1: [ child-0: [ word: likes ] ]
  child-0: [ child-0: [ word: he ] ]
]

> he likes
Parsed 0 trees

> he said that he likes her
Parsed 1 tree
(0..6: S
  (0..1: N (0..1: he))
  (1..2: CV (1..2: said))
  (2..3: Comp (2..3: that))
  (3..6: S
    (3..4: N (3..4: he))
    (4..5: TV (4..5: likes))
    (5..6: N (5..6: her))))
[
  child-0: [ child-0: [ word: he ] ]
  child-2: [ child-0: [ word: that ] ]
  child-1: [ child-0: [ word: said ] ]
  child-3: [
    child-2: [ child-0: [ word: her ] ]
    child-1: [ child-0: [ word: likes ] ]
    child-0: [ child-0: [ word: he ] ]
  ]
]

> he said that he
Parsed 0 trees

This grammar already parses some correct sentences, and blocks some trivially incorrect ones. However, it doesn't care about number, case, or reflexives right now:

> she likes himself  // unbound reflexive pronoun
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: she))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: himself)))
[
  child-0: [ child-0: [ word: she ] ]
  child-2: [ child-0: [ word: himself ] ]
  child-1: [ child-0: [ word: likes ] ]
]

> him like her  // incorrect case on the subject pronoun, should be nominative
                // (he) instead of accusative (him)
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: him))
  (1..2: TV (1..2: like))
  (2..3: N (2..3: her)))
[
  child-0: [ child-0: [ word: him ] ]
  child-1: [ child-0: [ word: like ] ]
  child-2: [ child-0: [ word: her ] ]
]

> he like her  // incorrect verb number agreement
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: he))
  (1..2: TV (1..2: like))
  (2..3: N (2..3: her)))
[
  child-2: [ child-0: [ word: her ] ]
  child-1: [ child-0: [ word: like ] ]
  child-0: [ child-0: [ word: he ] ]
]

To fix this, we need to add features to our lexicon, and restrict the sentence rules based on features.

Features are added with square brackets, and are key: value pairs separated by commas. **top** is a special feature value, which basically means "unspecified" -- we'll come back to it later. Features that are unspecified are also assumed to have a **top** value, but sometimes explicitly stating top is more clear.

/// Pronouns
// The added features are:
// * num: sg or pl, whether this noun wants a singular verb (likes) or
//   a plural verb (like). note this is grammatical number, so for example
//   singular they takes plural agreement ("they like X", not *"they likes X")
// * case: nom or acc, whether this noun is nominative or accusative case.
//   nominative case goes in the subject, and accusative in the object.
//   e.g., "he fell" and "she likes him", not *"him fell" and *"her likes he"
// * pron: he, she, they, or ref -- what type of pronoun this is
// * needs_pron: whether this is a reflexive that needs to bind to another
//   pronoun.
N[ num: sg, case: nom, pron: he ]                    -> he
N[ num: sg, case: acc, pron: he ]                    -> him
N[ num: sg, case: acc, pron: ref, needs_pron: he ]   -> himself
N[ num: sg, case: nom, pron: she ]                   -> she
N[ num: sg, case: acc, pron: she ]                   -> her
N[ num: sg, case: acc, pron: ref, needs_pron: she]   -> herself
N[ num: pl, case: nom, pron: they ]                  -> they
N[ num: pl, case: acc, pron: they ]                  -> them
N[ num: pl, case: acc, pron: ref, needs_pron: they ] -> themselves
N[ num: sg, case: acc, pron: ref, needs_pron: they ] -> themself

// Names
// The added features are:
// * num: sg, as people are singular ("mary likes her" / *"mary like her")
// * case: **top**, as names can be both subjects and objects
//   ("mary likes her" / "she likes mary")
// * pron: whichever pronoun the person uses for reflexive agreement
//   mary    pron: she  => mary likes herself
//   sue     pron: they => sue likes themself
//   takeshi pron: he   => takeshi likes himself
N[ num: sg, case: **top**, pron: she ]  -> mary
N[ num: sg, case: **top**, pron: they ] -> sue
N[ num: sg, case: **top**, pron: he ]   -> takeshi
N[ num: sg, case: **top**, pron: he ]   -> robert

// Complementizer doesn't need features
Comp -> that

// Verbs -- intransitive, transitive, and clausal
// The added features are:
// * num: sg, pl, or **top** -- to match the noun numbers.
//   **top** will match either sg or pl, as past-tense verbs in English
//   don't agree in number: "he fell" and "they fell" are both fine
// * tense: past or nonpast -- this won't be used for agreement, but will be
//   copied into the final feature structure, and the client code could do
//   something with it
IV[ num:      sg, tense: nonpast ] -> falls
IV[ num:      pl, tense: nonpast ] -> fall
IV[ num: **top**, tense: past ]    -> fell

TV[ num:      sg, tense: nonpast ] -> likes
TV[ num:      pl, tense: nonpast ] -> like
TV[ num: **top**, tense: past ]    -> liked

CV[ num:      sg, tense: nonpast ] -> says
CV[ num:      pl, tense: nonpast ] -> say
CV[ num: **top**, tense: past ]    -> said

Now that our lexicon is updated with features, we can update our sentence rules to constrain parsing based on those features. This uses two new features, tags and unification. Tags allow features to be associated between nodes in a rule, and unification controls how those features are compatible. The rules for unification are:

1. A string feature can unify with a string feature with the same value
2. A top feature can unify with anything, and the nodes are merged
3. A complex feature ([ ... ] structure) is recursively unified with another complex feature.

If unification fails anywhere, the parse is aborted and the tree is discarded. This allows the programmer to discard trees if features don't match.

// Sentence rules
// Intransitive verb:
// * Subject must be nominative case
// * Subject and verb must agree in number (copied through #1)
S -> N[ case: nom, num: #1 ] IV[ num: #1 ]
// Transitive verb:
// * Subject must be nominative case
// * Subject and verb must agree in number (copied through #2)
// * If there's a reflexive in the object position, make sure its `needs_pron`
//   feature matches the subject's `pron` feature. If the object isn't a
//   reflexive, then its `needs_pron` feature will implicitly be `**top**`, so
//   will unify with anything.
S -> N[ case: nom, pron: #1, num: #2 ] TV[ num: #2 ] N[ case: acc, needs_pron: #1 ]
// Clausal verb:
// * Subject must be nominative case
// * Subject and verb must agree in number (copied through #1)
// * Reflexives can't cross clause boundaries (*"He said that she likes himself"),
//   so we can ignore reflexives and delegate to inner clause rule
S -> N[ case: nom, num: #1 ] CV[ num: #1 ] Comp S

Now that we have this augmented grammar (available as examples/reflexives.fgr), we can try it out and see that it rejects illicit sentences that were previously accepted, while still accepting valid ones:

> he fell
Parsed 1 tree
(0..2: S
  (0..1: N (0..1: he))
  (1..2: IV (1..2: fell)))
[
  child-1: [
    child-0: [ word: fell ]
    num: #0 sg
    tense: past
  ]
  child-0: [
    pron: he
    case: nom
    num: #0
    child-0: [ word: he ]
  ]
]

> he like him
Parsed 0 trees

> he likes himself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: he))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: himself)))
[
  child-1: [
    num: #0 sg
    child-0: [ word: likes ]
    tense: nonpast
  ]
  child-2: [
    needs_pron: #1 he
    num: sg
    child-0: [ word: himself ]
    pron: ref
    case: acc
  ]
  child-0: [
    child-0: [ word: he ]
    pron: #1
    num: #0
    case: nom
  ]
]

> he likes herself
Parsed 0 trees

> mary likes herself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: mary))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: herself)))
[
  child-0: [
    pron: #0 she
    num: #1 sg
    case: nom
    child-0: [ word: mary ]
  ]
  child-1: [
    tense: nonpast
    child-0: [ word: likes ]
    num: #1
  ]
  child-2: [
    child-0: [ word: herself ]
    num: sg
    pron: ref
    case: acc
    needs_pron: #0
  ]
]

> mary likes themself
Parsed 0 trees

> sue likes themself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: sue))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: themself)))
[
  child-0: [
    pron: #0 they
    child-0: [ word: sue ]
    case: nom
    num: #1 sg
  ]
  child-1: [
    tense: nonpast
    num: #1
    child-0: [ word: likes ]
  ]
  child-2: [
    needs_pron: #0
    case: acc
    pron: ref
    child-0: [ word: themself ]
    num: sg
  ]
]

> sue likes himself
Parsed 0 trees

If this is interesting to you and you want to learn more, you can check out my blog series, the excellent textbook Syntactic Theory: A Formal Introduction (2nd ed.), and the DELPH-IN project, whose work on the LKB inspired this simplified version.

Using from code

I need to write this section in more detail, but if you're comfortable with Rust, I suggest looking through the codebase. It's not perfect, it started as one of my first Rust projects (after migrating through F# -> TypeScript -> C in search of the right performance/ergonomics tradeoff), and it could use more tests, but overall it's not too bad.

Basically, the processing pipeline is:

1. Make a Grammar struct

• Grammar is defined in rules.rs.
• The easiest way to make a Grammar is Grammar::parse_from_file, which is mostly a hand-written recusive descent parser in parse_grammar.rs. Yes, I recognize the irony here.

1. It takes input (in Grammar::parse, which does everything for you, or Grammar::parse_chart, which just does the chart)
2. The input is first chart-parsed in earley.rs
3. Then, a forest is built from the chart, in forest.rs, using an algorithm I found in a very useful blog series I forget the URL for, because the algorithms in the academic literature for this are... weird.
4. Finally, the feature unification is used to prune the forest down to only valid trees. It would be more efficient to do this during parsing, but meh.

The most interesting thing you can do via code and not via the CLI is probably getting at the raw feature DAG, as that would let you do things like pronoun coreference. The DAG code is in featurestructure.rs, and should be fairly approachable -- there's a lot of Rust ceremony around Rc<RefCell<...>> because using an arena allocation crate seemed too harlike overkill, but that is somewhat mitigated by the NodeRef type alias. Hit me up at https://vgel.me/contact if you need help with anything here!

Download Details:
Author: vgel
Source Code: https://github.com/vgel/treebender
License: MIT License

#rust  #machinelearning