Elastic Principal Component Regression¶
Warping Invariant PCR Regression using SRSF
moduleauthor:: J. Derek Tucker <jdtuck@sandia.gov>
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class
pcr_regression.
elastic_lpcr_regression
(f, y, time)[source]¶ This class provides elastic logistic pcr regression for functional data using the SRVF framework accounting for warping
Usage: obj = elastic_lpcr_regression(f,y,time)
Parameters: - f – (M,N) % matrix defining N functions of M samples
- y – response vector of length N (-1/1)
- warp_data – fdawarp object of alignment
- pca – class dependent on fPCA method used object of fPCA
:param information :param alpha: intercept :param b: coefficient vector :param Loss: logistic loss :param PC: probability of classification :param ylabels: predicted labels
Author : J. D. Tucker (JDT) <jdtuck AT sandia.gov> Date : 18-Mar-2018
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calc_model
(pca_method='combined', no=5, smooth_data=False, sparam=25, parallel=False)[source]¶ This function identifies a logistic regression model with phase-variability using elastic pca
Parameters: - pca_method – string specifing pca method (options = “combined”, “vert”, or “horiz”, default = “combined”)
- no – scalar specify number of principal components (default=5)
- smooth_data – smooth data using box filter (default = F)
- sparam – number of times to apply box filter (default = 25)
- parallel – calculate in parallel (default = F)
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predict
(newdata=None)[source]¶ This function performs prediction on regression model on new data if available or current stored data in object Usage: obj.predict()
obj.predict(newdata)Parameters: - newdata (dict) – dict containing new data for prediction (needs the keys below, if None predicts on training data)
- f – (M,N) matrix of functions
- time – vector of time points
- y – truth if available
- smooth – smooth data if needed
- sparam – number of times to run filter
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class
pcr_regression.
elastic_mlpcr_regression
(f, y, time)[source]¶ This class provides elastic multinomial logistic pcr regression for functional data using the SRVF framework accounting for warping
Usage: obj = elastic_mlpcr_regression(f,y,time)
Parameters: - f – (M,N) % matrix defining N functions of M samples
- y – response vector of length N
- Y – coded label matrix
- warp_data – fdawarp object of alignment
- pca – class dependent on fPCA method used object of fPCA
:param information :param alpha: intercept :param b: coefficient vector :param Loss: logistic loss :param PC: probability of classification :param ylabels: predicted labels :param
Author : J. D. Tucker (JDT) <jdtuck AT sandia.gov> Date : 18-Mar-2018
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calc_model
(pca_method='combined', no=5, smooth_data=False, sparam=25, parallel=False)[source]¶ This function identifies a logistic regression model with phase-variability using elastic pca
Parameters: - f (np.ndarray) – numpy ndarray of shape (M,N) of N functions with M samples
- y – numpy array of N responses
- time (np.ndarray) – vector of size M describing the sample points
- pca_method – string specifing pca method (options = “combined”, “vert”, or “horiz”, default = “combined”)
- no – scalar specify number of principal components (default=5)
- smooth_data – smooth data using box filter (default = F)
- sparam – number of times to apply box filter (default = 25)
- parallel – run model in parallel (default = F)
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predict
(newdata=None)[source]¶ This function performs prediction on regression model on new data if available or current stored data in object Usage: obj.predict()
obj.predict(newdata)Parameters: - newdata (dict) – dict containing new data for prediction (needs the keys below, if None predicts on training data)
- f – (M,N) matrix of functions
- time – vector of time points
- y – truth if available
- smooth – smooth data if needed
- sparam – number of times to run filter
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class
pcr_regression.
elastic_pcr_regression
(f, y, time)[source]¶ This class provides elastic pcr regression for functional data using the SRVF framework accounting for warping
Usage: obj = elastic_pcr_regression(f,y,time)
Parameters: - f – (M,N) % matrix defining N functions of M samples
- y – response vector of length N
- warp_data – fdawarp object of alignment
- pca – class dependent on fPCA method used object of fPCA
- alpha – intercept
- b – coefficient vector
- SSE – sum of squared errors
Author : J. D. Tucker (JDT) <jdtuck AT sandia.gov> Date : 18-Mar-2018
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calc_model
(pca_method='combined', no=5, smooth_data=False, sparam=25, parallel=False, C=None)[source]¶ This function identifies a regression model with phase-variability using elastic pca
Parameters: - pca_method – string specifing pca method (options = “combined”, “vert”, or “horiz”, default = “combined”)
- no – scalar specify number of principal components (default=5)
- smooth_data – smooth data using box filter (default = F)
- sparam – number of times to apply box filter (default = 25)
- parallel – run in parallel (default = F)
- C – scale balance parameter for combined method (default = None)
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predict
(newdata=None)[source]¶ This function performs prediction on regression model on new data if available or current stored data in object Usage: obj.predict()
obj.predict(newdata)Parameters: - newdata (dict) – dict containing new data for prediction (needs the keys below, if None predicts on training data)
- f – (M,N) matrix of functions
- time – vector of time points
- y – truth if available
- smooth – smooth data if needed
- sparam – number of times to run filter