initial commit
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695
venv/lib/python3.11/site-packages/cv2/ml/__init__.pyi
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695
venv/lib/python3.11/site-packages/cv2/ml/__init__.pyi
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__all__: list[str] = []
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import cv2
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import cv2.typing
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import typing as _typing
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# Enumerations
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VAR_NUMERICAL: int
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VAR_ORDERED: int
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VAR_CATEGORICAL: int
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VariableTypes = int
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"""One of [VAR_NUMERICAL, VAR_ORDERED, VAR_CATEGORICAL]"""
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TEST_ERROR: int
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TRAIN_ERROR: int
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ErrorTypes = int
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"""One of [TEST_ERROR, TRAIN_ERROR]"""
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ROW_SAMPLE: int
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COL_SAMPLE: int
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SampleTypes = int
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"""One of [ROW_SAMPLE, COL_SAMPLE]"""
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StatModel_UPDATE_MODEL: int
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STAT_MODEL_UPDATE_MODEL: int
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StatModel_RAW_OUTPUT: int
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STAT_MODEL_RAW_OUTPUT: int
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StatModel_COMPRESSED_INPUT: int
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STAT_MODEL_COMPRESSED_INPUT: int
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StatModel_PREPROCESSED_INPUT: int
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STAT_MODEL_PREPROCESSED_INPUT: int
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StatModel_Flags = int
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"""One of [StatModel_UPDATE_MODEL, STAT_MODEL_UPDATE_MODEL, StatModel_RAW_OUTPUT, STAT_MODEL_RAW_OUTPUT, StatModel_COMPRESSED_INPUT, STAT_MODEL_COMPRESSED_INPUT, StatModel_PREPROCESSED_INPUT, STAT_MODEL_PREPROCESSED_INPUT]"""
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KNearest_BRUTE_FORCE: int
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KNEAREST_BRUTE_FORCE: int
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KNearest_KDTREE: int
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KNEAREST_KDTREE: int
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KNearest_Types = int
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"""One of [KNearest_BRUTE_FORCE, KNEAREST_BRUTE_FORCE, KNearest_KDTREE, KNEAREST_KDTREE]"""
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SVM_C_SVC: int
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SVM_NU_SVC: int
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SVM_ONE_CLASS: int
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SVM_EPS_SVR: int
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SVM_NU_SVR: int
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SVM_Types = int
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"""One of [SVM_C_SVC, SVM_NU_SVC, SVM_ONE_CLASS, SVM_EPS_SVR, SVM_NU_SVR]"""
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SVM_CUSTOM: int
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SVM_LINEAR: int
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SVM_POLY: int
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SVM_RBF: int
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SVM_SIGMOID: int
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SVM_CHI2: int
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SVM_INTER: int
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SVM_KernelTypes = int
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"""One of [SVM_CUSTOM, SVM_LINEAR, SVM_POLY, SVM_RBF, SVM_SIGMOID, SVM_CHI2, SVM_INTER]"""
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SVM_C: int
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SVM_GAMMA: int
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SVM_P: int
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SVM_NU: int
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SVM_COEF: int
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SVM_DEGREE: int
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SVM_ParamTypes = int
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"""One of [SVM_C, SVM_GAMMA, SVM_P, SVM_NU, SVM_COEF, SVM_DEGREE]"""
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EM_COV_MAT_SPHERICAL: int
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EM_COV_MAT_DIAGONAL: int
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EM_COV_MAT_GENERIC: int
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EM_COV_MAT_DEFAULT: int
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EM_Types = int
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"""One of [EM_COV_MAT_SPHERICAL, EM_COV_MAT_DIAGONAL, EM_COV_MAT_GENERIC, EM_COV_MAT_DEFAULT]"""
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EM_DEFAULT_NCLUSTERS: int
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EM_DEFAULT_MAX_ITERS: int
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EM_START_E_STEP: int
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EM_START_M_STEP: int
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EM_START_AUTO_STEP: int
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DTrees_PREDICT_AUTO: int
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DTREES_PREDICT_AUTO: int
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DTrees_PREDICT_SUM: int
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DTREES_PREDICT_SUM: int
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DTrees_PREDICT_MAX_VOTE: int
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DTREES_PREDICT_MAX_VOTE: int
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DTrees_PREDICT_MASK: int
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DTREES_PREDICT_MASK: int
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DTrees_Flags = int
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"""One of [DTrees_PREDICT_AUTO, DTREES_PREDICT_AUTO, DTrees_PREDICT_SUM, DTREES_PREDICT_SUM, DTrees_PREDICT_MAX_VOTE, DTREES_PREDICT_MAX_VOTE, DTrees_PREDICT_MASK, DTREES_PREDICT_MASK]"""
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Boost_DISCRETE: int
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BOOST_DISCRETE: int
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Boost_REAL: int
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BOOST_REAL: int
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Boost_LOGIT: int
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BOOST_LOGIT: int
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Boost_GENTLE: int
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BOOST_GENTLE: int
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Boost_Types = int
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"""One of [Boost_DISCRETE, BOOST_DISCRETE, Boost_REAL, BOOST_REAL, Boost_LOGIT, BOOST_LOGIT, Boost_GENTLE, BOOST_GENTLE]"""
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ANN_MLP_BACKPROP: int
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ANN_MLP_RPROP: int
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ANN_MLP_ANNEAL: int
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ANN_MLP_TrainingMethods = int
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"""One of [ANN_MLP_BACKPROP, ANN_MLP_RPROP, ANN_MLP_ANNEAL]"""
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ANN_MLP_IDENTITY: int
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ANN_MLP_SIGMOID_SYM: int
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ANN_MLP_GAUSSIAN: int
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ANN_MLP_RELU: int
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ANN_MLP_LEAKYRELU: int
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ANN_MLP_ActivationFunctions = int
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"""One of [ANN_MLP_IDENTITY, ANN_MLP_SIGMOID_SYM, ANN_MLP_GAUSSIAN, ANN_MLP_RELU, ANN_MLP_LEAKYRELU]"""
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ANN_MLP_UPDATE_WEIGHTS: int
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ANN_MLP_NO_INPUT_SCALE: int
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ANN_MLP_NO_OUTPUT_SCALE: int
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ANN_MLP_TrainFlags = int
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"""One of [ANN_MLP_UPDATE_WEIGHTS, ANN_MLP_NO_INPUT_SCALE, ANN_MLP_NO_OUTPUT_SCALE]"""
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LogisticRegression_REG_DISABLE: int
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LOGISTIC_REGRESSION_REG_DISABLE: int
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LogisticRegression_REG_L1: int
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LOGISTIC_REGRESSION_REG_L1: int
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LogisticRegression_REG_L2: int
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LOGISTIC_REGRESSION_REG_L2: int
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LogisticRegression_RegKinds = int
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"""One of [LogisticRegression_REG_DISABLE, LOGISTIC_REGRESSION_REG_DISABLE, LogisticRegression_REG_L1, LOGISTIC_REGRESSION_REG_L1, LogisticRegression_REG_L2, LOGISTIC_REGRESSION_REG_L2]"""
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LogisticRegression_BATCH: int
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LOGISTIC_REGRESSION_BATCH: int
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LogisticRegression_MINI_BATCH: int
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LOGISTIC_REGRESSION_MINI_BATCH: int
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LogisticRegression_Methods = int
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"""One of [LogisticRegression_BATCH, LOGISTIC_REGRESSION_BATCH, LogisticRegression_MINI_BATCH, LOGISTIC_REGRESSION_MINI_BATCH]"""
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SVMSGD_SGD: int
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SVMSGD_ASGD: int
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SVMSGD_SvmsgdType = int
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"""One of [SVMSGD_SGD, SVMSGD_ASGD]"""
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SVMSGD_SOFT_MARGIN: int
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SVMSGD_HARD_MARGIN: int
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SVMSGD_MarginType = int
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"""One of [SVMSGD_SOFT_MARGIN, SVMSGD_HARD_MARGIN]"""
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# Classes
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class ParamGrid:
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minVal: float
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maxVal: float
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logStep: float
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# Functions
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@classmethod
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def create(cls, minVal: float = ..., maxVal: float = ..., logstep: float = ...) -> ParamGrid: ...
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class TrainData:
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# Functions
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def getLayout(self) -> int: ...
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def getNTrainSamples(self) -> int: ...
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def getNTestSamples(self) -> int: ...
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def getNSamples(self) -> int: ...
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def getNVars(self) -> int: ...
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def getNAllVars(self) -> int: ...
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@_typing.overload
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def getSample(self, varIdx: cv2.typing.MatLike, sidx: int, buf: float) -> None: ...
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@_typing.overload
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def getSample(self, varIdx: cv2.UMat, sidx: int, buf: float) -> None: ...
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def getSamples(self) -> cv2.typing.MatLike: ...
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def getMissing(self) -> cv2.typing.MatLike: ...
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def getTrainSamples(self, layout: int = ..., compressSamples: bool = ..., compressVars: bool = ...) -> cv2.typing.MatLike: ...
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def getTrainResponses(self) -> cv2.typing.MatLike: ...
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def getTrainNormCatResponses(self) -> cv2.typing.MatLike: ...
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def getTestResponses(self) -> cv2.typing.MatLike: ...
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def getTestNormCatResponses(self) -> cv2.typing.MatLike: ...
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def getResponses(self) -> cv2.typing.MatLike: ...
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def getNormCatResponses(self) -> cv2.typing.MatLike: ...
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def getSampleWeights(self) -> cv2.typing.MatLike: ...
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def getTrainSampleWeights(self) -> cv2.typing.MatLike: ...
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def getTestSampleWeights(self) -> cv2.typing.MatLike: ...
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def getVarIdx(self) -> cv2.typing.MatLike: ...
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def getVarType(self) -> cv2.typing.MatLike: ...
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def getVarSymbolFlags(self) -> cv2.typing.MatLike: ...
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def getResponseType(self) -> int: ...
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def getTrainSampleIdx(self) -> cv2.typing.MatLike: ...
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def getTestSampleIdx(self) -> cv2.typing.MatLike: ...
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@_typing.overload
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def getValues(self, vi: int, sidx: cv2.typing.MatLike, values: float) -> None: ...
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@_typing.overload
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def getValues(self, vi: int, sidx: cv2.UMat, values: float) -> None: ...
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def getDefaultSubstValues(self) -> cv2.typing.MatLike: ...
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def getCatCount(self, vi: int) -> int: ...
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def getClassLabels(self) -> cv2.typing.MatLike: ...
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def getCatOfs(self) -> cv2.typing.MatLike: ...
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def getCatMap(self) -> cv2.typing.MatLike: ...
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def setTrainTestSplit(self, count: int, shuffle: bool = ...) -> None: ...
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def setTrainTestSplitRatio(self, ratio: float, shuffle: bool = ...) -> None: ...
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def shuffleTrainTest(self) -> None: ...
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def getTestSamples(self) -> cv2.typing.MatLike: ...
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def getNames(self, names: _typing.Sequence[str]) -> None: ...
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@staticmethod
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def getSubVector(vec: cv2.typing.MatLike, idx: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
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@staticmethod
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def getSubMatrix(matrix: cv2.typing.MatLike, idx: cv2.typing.MatLike, layout: int) -> cv2.typing.MatLike: ...
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@classmethod
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@_typing.overload
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def create(cls, samples: cv2.typing.MatLike, layout: int, responses: cv2.typing.MatLike, varIdx: cv2.typing.MatLike | None = ..., sampleIdx: cv2.typing.MatLike | None = ..., sampleWeights: cv2.typing.MatLike | None = ..., varType: cv2.typing.MatLike | None = ...) -> TrainData: ...
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@classmethod
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@_typing.overload
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def create(cls, samples: cv2.UMat, layout: int, responses: cv2.UMat, varIdx: cv2.UMat | None = ..., sampleIdx: cv2.UMat | None = ..., sampleWeights: cv2.UMat | None = ..., varType: cv2.UMat | None = ...) -> TrainData: ...
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class StatModel(cv2.Algorithm):
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# Functions
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def getVarCount(self) -> int: ...
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def empty(self) -> bool: ...
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def isTrained(self) -> bool: ...
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def isClassifier(self) -> bool: ...
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@_typing.overload
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def train(self, trainData: TrainData, flags: int = ...) -> bool: ...
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@_typing.overload
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def train(self, samples: cv2.typing.MatLike, layout: int, responses: cv2.typing.MatLike) -> bool: ...
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@_typing.overload
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def train(self, samples: cv2.UMat, layout: int, responses: cv2.UMat) -> bool: ...
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@_typing.overload
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def calcError(self, data: TrainData, test: bool, resp: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike]: ...
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@_typing.overload
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def calcError(self, data: TrainData, test: bool, resp: cv2.UMat | None = ...) -> tuple[float, cv2.UMat]: ...
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@_typing.overload
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def predict(self, samples: cv2.typing.MatLike, results: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ...
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@_typing.overload
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def predict(self, samples: cv2.UMat, results: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat]: ...
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class NormalBayesClassifier(StatModel):
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# Functions
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@_typing.overload
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def predictProb(self, inputs: cv2.typing.MatLike, outputs: cv2.typing.MatLike | None = ..., outputProbs: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike]: ...
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@_typing.overload
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def predictProb(self, inputs: cv2.UMat, outputs: cv2.UMat | None = ..., outputProbs: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat, cv2.UMat]: ...
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@classmethod
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def create(cls) -> NormalBayesClassifier: ...
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@classmethod
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def load(cls, filepath: str, nodeName: str = ...) -> NormalBayesClassifier: ...
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class KNearest(StatModel):
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# Functions
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def getDefaultK(self) -> int: ...
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def setDefaultK(self, val: int) -> None: ...
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def getIsClassifier(self) -> bool: ...
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def setIsClassifier(self, val: bool) -> None: ...
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def getEmax(self) -> int: ...
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def setEmax(self, val: int) -> None: ...
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def getAlgorithmType(self) -> int: ...
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def setAlgorithmType(self, val: int) -> None: ...
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@_typing.overload
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def findNearest(self, samples: cv2.typing.MatLike, k: int, results: cv2.typing.MatLike | None = ..., neighborResponses: cv2.typing.MatLike | None = ..., dist: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
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@_typing.overload
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def findNearest(self, samples: cv2.UMat, k: int, results: cv2.UMat | None = ..., neighborResponses: cv2.UMat | None = ..., dist: cv2.UMat | None = ...) -> tuple[float, cv2.UMat, cv2.UMat, cv2.UMat]: ...
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@classmethod
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def create(cls) -> KNearest: ...
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@classmethod
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def load(cls, filepath: str) -> KNearest: ...
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class SVM(StatModel):
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# Functions
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def getType(self) -> int: ...
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def setType(self, val: int) -> None: ...
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def getGamma(self) -> float: ...
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def setGamma(self, val: float) -> None: ...
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def getCoef0(self) -> float: ...
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def setCoef0(self, val: float) -> None: ...
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def getDegree(self) -> float: ...
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def setDegree(self, val: float) -> None: ...
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def getC(self) -> float: ...
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def setC(self, val: float) -> None: ...
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def getNu(self) -> float: ...
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def setNu(self, val: float) -> None: ...
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def getP(self) -> float: ...
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def setP(self, val: float) -> None: ...
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def getClassWeights(self) -> cv2.typing.MatLike: ...
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def setClassWeights(self, val: cv2.typing.MatLike) -> None: ...
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def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
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def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
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def getKernelType(self) -> int: ...
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def setKernel(self, kernelType: int) -> None: ...
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@_typing.overload
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def trainAuto(self, samples: cv2.typing.MatLike, layout: int, responses: cv2.typing.MatLike, kFold: int = ..., Cgrid: ParamGrid = ..., gammaGrid: ParamGrid = ..., pGrid: ParamGrid = ..., nuGrid: ParamGrid = ..., coeffGrid: ParamGrid = ..., degreeGrid: ParamGrid = ..., balanced: bool = ...) -> bool: ...
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@_typing.overload
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def trainAuto(self, samples: cv2.UMat, layout: int, responses: cv2.UMat, kFold: int = ..., Cgrid: ParamGrid = ..., gammaGrid: ParamGrid = ..., pGrid: ParamGrid = ..., nuGrid: ParamGrid = ..., coeffGrid: ParamGrid = ..., degreeGrid: ParamGrid = ..., balanced: bool = ...) -> bool: ...
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def getSupportVectors(self) -> cv2.typing.MatLike: ...
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def getUncompressedSupportVectors(self) -> cv2.typing.MatLike: ...
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@_typing.overload
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def getDecisionFunction(self, i: int, alpha: cv2.typing.MatLike | None = ..., svidx: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike]: ...
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@_typing.overload
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def getDecisionFunction(self, i: int, alpha: cv2.UMat | None = ..., svidx: cv2.UMat | None = ...) -> tuple[float, cv2.UMat, cv2.UMat]: ...
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@staticmethod
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def getDefaultGridPtr(param_id: int) -> ParamGrid: ...
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@classmethod
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def create(cls) -> SVM: ...
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@classmethod
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def load(cls, filepath: str) -> SVM: ...
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class EM(StatModel):
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# Functions
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def getClustersNumber(self) -> int: ...
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def setClustersNumber(self, val: int) -> None: ...
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def getCovarianceMatrixType(self) -> int: ...
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def setCovarianceMatrixType(self, val: int) -> None: ...
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def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
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def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
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def getWeights(self) -> cv2.typing.MatLike: ...
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def getMeans(self) -> cv2.typing.MatLike: ...
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||||
|
||||
def getCovs(self, covs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.typing.MatLike, results: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.UMat, results: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def predict2(self, sample: cv2.typing.MatLike, probs: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.Vec2d, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def predict2(self, sample: cv2.UMat, probs: cv2.UMat | None = ...) -> tuple[cv2.typing.Vec2d, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def trainEM(self, samples: cv2.typing.MatLike, logLikelihoods: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., probs: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def trainEM(self, samples: cv2.UMat, logLikelihoods: cv2.UMat | None = ..., labels: cv2.UMat | None = ..., probs: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def trainE(self, samples: cv2.typing.MatLike, means0: cv2.typing.MatLike, covs0: cv2.typing.MatLike | None = ..., weights0: cv2.typing.MatLike | None = ..., logLikelihoods: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., probs: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def trainE(self, samples: cv2.UMat, means0: cv2.UMat, covs0: cv2.UMat | None = ..., weights0: cv2.UMat | None = ..., logLikelihoods: cv2.UMat | None = ..., labels: cv2.UMat | None = ..., probs: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def trainM(self, samples: cv2.typing.MatLike, probs0: cv2.typing.MatLike, logLikelihoods: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., probs: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def trainM(self, samples: cv2.UMat, probs0: cv2.UMat, logLikelihoods: cv2.UMat | None = ..., labels: cv2.UMat | None = ..., probs: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> EM: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> EM: ...
|
||||
|
||||
|
||||
class DTrees(StatModel):
|
||||
# Functions
|
||||
def getMaxCategories(self) -> int: ...
|
||||
|
||||
def setMaxCategories(self, val: int) -> None: ...
|
||||
|
||||
def getMaxDepth(self) -> int: ...
|
||||
|
||||
def setMaxDepth(self, val: int) -> None: ...
|
||||
|
||||
def getMinSampleCount(self) -> int: ...
|
||||
|
||||
def setMinSampleCount(self, val: int) -> None: ...
|
||||
|
||||
def getCVFolds(self) -> int: ...
|
||||
|
||||
def setCVFolds(self, val: int) -> None: ...
|
||||
|
||||
def getUseSurrogates(self) -> bool: ...
|
||||
|
||||
def setUseSurrogates(self, val: bool) -> None: ...
|
||||
|
||||
def getUse1SERule(self) -> bool: ...
|
||||
|
||||
def setUse1SERule(self, val: bool) -> None: ...
|
||||
|
||||
def getTruncatePrunedTree(self) -> bool: ...
|
||||
|
||||
def setTruncatePrunedTree(self, val: bool) -> None: ...
|
||||
|
||||
def getRegressionAccuracy(self) -> float: ...
|
||||
|
||||
def setRegressionAccuracy(self, val: float) -> None: ...
|
||||
|
||||
def getPriors(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def setPriors(self, val: cv2.typing.MatLike) -> None: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> DTrees: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> DTrees: ...
|
||||
|
||||
|
||||
class RTrees(DTrees):
|
||||
# Functions
|
||||
def getCalculateVarImportance(self) -> bool: ...
|
||||
|
||||
def setCalculateVarImportance(self, val: bool) -> None: ...
|
||||
|
||||
def getActiveVarCount(self) -> int: ...
|
||||
|
||||
def setActiveVarCount(self, val: int) -> None: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
def getVarImportance(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
@_typing.overload
|
||||
def getVotes(self, samples: cv2.typing.MatLike, flags: int, results: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def getVotes(self, samples: cv2.UMat, flags: int, results: cv2.UMat | None = ...) -> cv2.UMat: ...
|
||||
|
||||
def getOOBError(self) -> float: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> RTrees: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> RTrees: ...
|
||||
|
||||
|
||||
class Boost(DTrees):
|
||||
# Functions
|
||||
def getBoostType(self) -> int: ...
|
||||
|
||||
def setBoostType(self, val: int) -> None: ...
|
||||
|
||||
def getWeakCount(self) -> int: ...
|
||||
|
||||
def setWeakCount(self, val: int) -> None: ...
|
||||
|
||||
def getWeightTrimRate(self) -> float: ...
|
||||
|
||||
def setWeightTrimRate(self, val: float) -> None: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> Boost: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> Boost: ...
|
||||
|
||||
|
||||
class ANN_MLP(StatModel):
|
||||
# Functions
|
||||
def setTrainMethod(self, method: int, param1: float = ..., param2: float = ...) -> None: ...
|
||||
|
||||
def getTrainMethod(self) -> int: ...
|
||||
|
||||
def setActivationFunction(self, type: int, param1: float = ..., param2: float = ...) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def setLayerSizes(self, _layer_sizes: cv2.typing.MatLike) -> None: ...
|
||||
@_typing.overload
|
||||
def setLayerSizes(self, _layer_sizes: cv2.UMat) -> None: ...
|
||||
|
||||
def getLayerSizes(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
def getBackpropWeightScale(self) -> float: ...
|
||||
|
||||
def setBackpropWeightScale(self, val: float) -> None: ...
|
||||
|
||||
def getBackpropMomentumScale(self) -> float: ...
|
||||
|
||||
def setBackpropMomentumScale(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDW0(self) -> float: ...
|
||||
|
||||
def setRpropDW0(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDWPlus(self) -> float: ...
|
||||
|
||||
def setRpropDWPlus(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDWMinus(self) -> float: ...
|
||||
|
||||
def setRpropDWMinus(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDWMin(self) -> float: ...
|
||||
|
||||
def setRpropDWMin(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDWMax(self) -> float: ...
|
||||
|
||||
def setRpropDWMax(self, val: float) -> None: ...
|
||||
|
||||
def getAnnealInitialT(self) -> float: ...
|
||||
|
||||
def setAnnealInitialT(self, val: float) -> None: ...
|
||||
|
||||
def getAnnealFinalT(self) -> float: ...
|
||||
|
||||
def setAnnealFinalT(self, val: float) -> None: ...
|
||||
|
||||
def getAnnealCoolingRatio(self) -> float: ...
|
||||
|
||||
def setAnnealCoolingRatio(self, val: float) -> None: ...
|
||||
|
||||
def getAnnealItePerStep(self) -> int: ...
|
||||
|
||||
def setAnnealItePerStep(self, val: int) -> None: ...
|
||||
|
||||
def getWeights(self, layerIdx: int) -> cv2.typing.MatLike: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> ANN_MLP: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str) -> ANN_MLP: ...
|
||||
|
||||
|
||||
class LogisticRegression(StatModel):
|
||||
# Functions
|
||||
def getLearningRate(self) -> float: ...
|
||||
|
||||
def setLearningRate(self, val: float) -> None: ...
|
||||
|
||||
def getIterations(self) -> int: ...
|
||||
|
||||
def setIterations(self, val: int) -> None: ...
|
||||
|
||||
def getRegularization(self) -> int: ...
|
||||
|
||||
def setRegularization(self, val: int) -> None: ...
|
||||
|
||||
def getTrainMethod(self) -> int: ...
|
||||
|
||||
def setTrainMethod(self, val: int) -> None: ...
|
||||
|
||||
def getMiniBatchSize(self) -> int: ...
|
||||
|
||||
def setMiniBatchSize(self, val: int) -> None: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.typing.MatLike, results: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.UMat, results: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat]: ...
|
||||
|
||||
def get_learnt_thetas(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> LogisticRegression: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> LogisticRegression: ...
|
||||
|
||||
|
||||
class SVMSGD(StatModel):
|
||||
# Functions
|
||||
def getWeights(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getShift(self) -> float: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> SVMSGD: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> SVMSGD: ...
|
||||
|
||||
def setOptimalParameters(self, svmsgdType: int = ..., marginType: int = ...) -> None: ...
|
||||
|
||||
def getSvmsgdType(self) -> int: ...
|
||||
|
||||
def setSvmsgdType(self, svmsgdType: int) -> None: ...
|
||||
|
||||
def getMarginType(self) -> int: ...
|
||||
|
||||
def setMarginType(self, marginType: int) -> None: ...
|
||||
|
||||
def getMarginRegularization(self) -> float: ...
|
||||
|
||||
def setMarginRegularization(self, marginRegularization: float) -> None: ...
|
||||
|
||||
def getInitialStepSize(self) -> float: ...
|
||||
|
||||
def setInitialStepSize(self, InitialStepSize: float) -> None: ...
|
||||
|
||||
def getStepDecreasingPower(self) -> float: ...
|
||||
|
||||
def setStepDecreasingPower(self, stepDecreasingPower: float) -> None: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user