The idea of caching items for fast retrieval goes back nearly to the beginning of the computer science. We also use that idea and use a LRU cache for storing morphological analyses of surface forms. Before analyzing a surface form, we first look up to the cache, and if there is an hit, we just take the analyses from the cache. If there is a miss, we analyze the surface form and put the morphological analyses of that surface form in the LRU cache. As can be expected, the speed of the caching mechanism surely depends on the size of the cache.

You can also see Cython, Java, C, C++, Swift, Php, Js, or C# repository.

• Python 3.7 or higher
• Git

To check if you have a compatible version of Python installed, use the following command:

python -V

You can find the latest version of Python here.

Install the latest version of Git.

pip3 install NlpToolkit-DataStructure

In order to work on code, create a fork from GitHub page. Use Git for cloning the code to your local or below line for Ubuntu:

git clone <your-fork-git-link>

A directory called DataStructure will be created. Or you can use below link for exploring the code:

git clone https://github.com/starlangsoftware/DataStructure-Py.git

Steps for opening the cloned project:

• Start IDE
• Select File | Open from main menu
• Choose DataStructure-PY file
• Select open as project option
• Couple of seconds, dependencies will be downloaded.

• CounterHashMap
• LRUCache

CounterHashMap bir veri tipinin kaç kere geçtiğini hafızada tutmak için kullanılmaktadır.

Bir CounterHashMap yaratmak için

a = CounterHashMap()

Hafızaya veri eklemek için

put(self, key: object)

Örneğin,

a.put("ali");

Bu aşamanın ardından "ali" nin sayacı 1 olur.

Hafızaya o veriyi birden fazla kez eklemek için

putNTimes(self, key: object, N: int)

Örneğin,

a.putNTimes("veli", 5)

Bu aşamanın ardından "ali"'nin sayacı 5 olur.

Hafızada o verinin kaç kere geçtiğini bulmak için

count(self, key: object) -> int

Örneğin, "veli" nin kaç kere geçtiğini bulmak için

kacKere = a.count("veli")

Bu aşamanın ardından kacKere değişkeninin değeri 5 olur.

Hafızada hangi verinin en çok geçtiğini bulmak için

max(self) -> object

Örneğin,

kelime = a.max()

Bu aşamanın ardından kelime "veli" olur.

LRUCache veri cachelemek için kullanılan bir veri yapısıdır. LRUCache en yakın zamanda kullanılan verileri öncelikli olarak hafızada tutar. Bir LRUCache yaratmak için

LRUCache(self, cacheSize: int)

kullanılır. cacheSize burada cachelenecek verinin büyüklüğünün limitini göstermektedir.

Cache'e bir veri eklemek için

add(self, key: object, data: object)

kullanılır. data burada eklenecek veriyi, key anahtar göstergeyi göstermektedir.

Cache'de bir veri var mı diye kontrol etmek için

contains(self, key: object) -> bool

kullanılır.

Cache'deki veriyi anahtarına göre getirmek için

get(self, key: object) -> object

kullanılır.

1. Do not forget to set package list. All subfolders should be added to the package list.

    packages=['Classification', 'Classification.Model', 'Classification.Model.DecisionTree',
              'Classification.Model.Ensemble', 'Classification.Model.NeuralNetwork',
              'Classification.Model.NonParametric', 'Classification.Model.Parametric',
              'Classification.Filter', 'Classification.DataSet', 'Classification.Instance', 'Classification.Attribute',
              'Classification.Parameter', 'Classification.Experiment',
              'Classification.Performance', 'Classification.InstanceList', 'Classification.DistanceMetric',
              'Classification.StatisticalTest', 'Classification.FeatureSelection'],

2. Package name should be lowercase and only may include _ character.

    name='nlptoolkit_math',

1. Do not forget to comment each function.

    def __broadcast_shape(self, shape1: Tuple[int, ...], shape2: Tuple[int, ...]) -> Tuple[int, ...]:
        """
        Determines the broadcasted shape of two tensors.

        :param shape1: Tuple representing the first tensor shape.
        :param shape2: Tuple representing the second tensor shape.
        :return: Tuple representing the broadcasted shape.
        """

2. Function names should follow caml case.

    def addItem(self, item: str):

3. Local variables should follow snake case.

	det = 1.0
	copy_of_matrix = copy.deepcopy(self)

4. Class variables should be declared in each file.

class Eigenvector(Vector):
    eigenvalue: float

5. Variable types should be defined for function parameters and class variables.

    def getIndex(self, item: str) -> int:

6. For abstract methods, use ABC package and declare them with @abstractmethod.

    @abstractmethod
    def train(self, train_set: list[Tensor]):
        pass

7. For private methods, use __ as prefix in their names.

    def __infer_shape(self, data: Union[List, List[List], List[List[List]]]) -> Tuple[int, ...]:

8. For private class variables, use __ as prefix in their names.

class Matrix(object):
    __row: int
    __col: int
    __values: list[list[float]]

9. Write __repr__ class methods as toString methods
10. Write getter and setter class methods.

    def getOptimizer(self) -> Optimizer:
        return self.optimizer
    def setValue(self, value: Optional[Tensor]) -> None:
        self._value = value

11. If there are multiple constructors for a class, define them as constructor1, constructor2, ..., then from the original constructor call these methods.

    def constructor1(self):
        self.__values = []
        self.__size = 0

    def constructor2(self, values: list):
        self.__values = values.copy()
        self.__size = len(values)

    def __init__(self,
                 valuesOrSize=None,
                 initial=None):
        if valuesOrSize is None:
            self.constructor1()
        elif isinstance(valuesOrSize, list):
            self.constructor2(valuesOrSize)

12. Extend test classes from unittest and use separate unit test methods.

class TensorTest(unittest.TestCase):

    def test_inferred_shape(self):
        a = Tensor([[1.0, 2.0], [3.0, 4.0]])
        self.assertEqual((2, 2), a.getShape())

    def test_shape(self):
        a = Tensor([1.0, 2.0, 3.0])
        self.assertEqual((3, ), a.getShape())

13. Enumerated types should be used when necessary as enum classes.

class AttributeType(Enum):
    """
    Continuous Attribute
    """
    CONTINUOUS = auto()
    """
    Discrete Attribute
    """
    DISCRETE = auto()