Python KMeans示例

示例#1

            else:
                inst = obj()

            yield inst


@pytest.mark.parametrize('estimator, check', [
    pytest.param(
        copy.deepcopy(estimator), check, id=f'{estimator}:{check.__name__}')
    for estimator in list(get_all_estimators()) + [
        feature_extraction.TFIDF(),
        linear_model.LogisticRegression(),
        preprocessing.StandardScaler() | linear_model.LinearRegression(),
        preprocessing.StandardScaler() | linear_model.PAClassifier(),
        preprocessing.StandardScaler()
        | multiclass.OneVsRestClassifier(linear_model.LogisticRegression()),
        preprocessing.StandardScaler()
        | multiclass.OneVsRestClassifier(linear_model.PAClassifier()),
        naive_bayes.GaussianNB(),
        preprocessing.StandardScaler(),
        cluster.KMeans(n_clusters=5, seed=42),
        preprocessing.MinMaxScaler(),
        preprocessing.MinMaxScaler() + preprocessing.StandardScaler(),
        preprocessing.PolynomialExtender(),
        feature_selection.VarianceThreshold(),
        feature_selection.SelectKBest(similarity=stats.PearsonCorrelation())
    ] for check in utils.estimator_checks.yield_checks(estimator)
])
def test_check_estimator(estimator, check):
    check(estimator)

示例#2

from sklearn.utils import estimator_checks

from creme import cluster
from creme import compat
from creme import feature_selection
from creme import linear_model
from creme import multiclass
from creme import naive_bayes
from creme import preprocessing
from creme import stats
from creme import utils

ESTIMATORS = [
    naive_bayes.GaussianNB(),
    preprocessing.StandardScaler(),
    cluster.KMeans(random_state=42),
    preprocessing.MinMaxScaler(),
    preprocessing.MinMaxScaler() + preprocessing.StandardScaler(),
    preprocessing.PolynomialExtender(),
    feature_selection.VarianceThreshold(),
    feature_selection.SelectKBest(similarity=stats.PearsonCorrelation())
]


@pytest.mark.parametrize('estimator', [
    pytest.param(copy.deepcopy(estimator), id=str(estimator))
    for estimator in ESTIMATORS
])
def test_sklearn_check_estimator(estimator):
    estimator_checks.check_estimator(
        compat.convert_creme_to_sklearn(estimator))

示例#3

文件： test_.py 项目： rajagurunath/creme

            else:
                inst = obj()

            yield inst


@pytest.mark.parametrize('estimator', [
    pytest.param(copy.deepcopy(estimator), id=str(estimator))
    for estimator in list(get_all_estimators()) + [
        feature_extraction.TFIDFVectorizer(),
        linear_model.LogisticRegression(),
        preprocessing.StandardScaler() | linear_model.LinearRegression(),
        preprocessing.StandardScaler() | linear_model.PAClassifier(),
        preprocessing.StandardScaler()
        | multiclass.OneVsRestClassifier(linear_model.LogisticRegression()),
        preprocessing.StandardScaler()
        | multiclass.OneVsRestClassifier(linear_model.PAClassifier()),
        naive_bayes.GaussianNB(),
        preprocessing.StandardScaler(),
        cluster.KMeans(n_clusters=5, random_state=42),
        preprocessing.MinMaxScaler(),
        preprocessing.MinMaxScaler() + preprocessing.StandardScaler(),
        preprocessing.PolynomialExtender(),
        feature_selection.VarianceThreshold(),
        feature_selection.SelectKBest(similarity=stats.PearsonCorrelation())
    ]
])
def test_check_estimator(estimator):
    utils.check_estimator(estimator)

示例#4

文件： test_sklearn.py 项目： zeta1999/creme

import pytest
from sklearn.utils import estimator_checks
from sklearn import linear_model as sk_linear_model

from creme import base
from creme import cluster
from creme import compat
from creme import linear_model
from creme import preprocessing


@pytest.mark.parametrize('estimator', [
    pytest.param(estimator, id=str(estimator))
    for estimator in [
        linear_model.LinearRegression(),
        linear_model.LogisticRegression(),
        preprocessing.StandardScaler(),
        cluster.KMeans(seed=42)
    ]
])
def test_creme_to_sklearn_check_estimator(estimator: base.Estimator):
    skl_estimator = compat.convert_creme_to_sklearn(estimator)
    estimator_checks.check_estimator(skl_estimator)


def test_sklearn_check_twoway():
    estimator = sk_linear_model.SGDRegressor()
    creme_estimator = compat.convert_sklearn_to_creme(estimator)
    skl_estimator = compat.convert_creme_to_sklearn(creme_estimator)
    estimator_checks.check_estimator(skl_estimator)

示例#5

文件： KMeans.py 项目： farzinsarvaramini/Node-Red-Incremental-Learning

import pickle
import json
import ast

#read configurations
config = json.loads(input())
savePath = config['savePath']
n_clusters = config['n_clusters']
halflife = config['halflife']
sigma = config['sigma']
mu = config['mu']
random_state = config['randomState']

k_means = cluster.KMeans(n_clusters=n_clusters,
                         halflife=halflife,
                         mu=mu,
                         sigma=sigma,
                         random_state=random_state)

output = {}

while True:

    #wait request
    data = input()

    Xi = json.loads(data)

    output["Predict"] = k_means.predict_one(Xi)

    model = k_means.fit_one(Xi)