class_docstr: Sigmoid regression model.
  
  Attributes
  ----------
  a_ : float
      The slope.
  
  b_ : float
      The intercept.
class_docstr_tok: ["Sigmoid","regression","model",".","Attributes","--","--","--","--","--","a_",":","float","The","slope",".","b_",":","float","The","intercept","."]
docstr: Predict new data by linear interpolation.
  
  Parameters
  ----------
  T : array-like, shape (n_samples,)
      Data to predict from.
  
  Returns
  -------
  T_ : array, shape (n_samples,)
      The predicted data.
docstr_tok: ["Predict","new","data","by","linear","interpolation",".","Parameters","--","--","--","--","--","T",":","array-like",",","shape","(","n_samples",",",")","Data","to","predict","from",".","Returns","--","--","--","-","T_",":","array",",","shape","(","n_samples",",",")","The","predicted","data","."]
code: def predict(self, T):
      """Predict new data by linear interpolation.
  
      Parameters
      ----------
      T : array-like, shape (n_samples,)
          Data to predict from.
  
      Returns
      -------
      T_ : array, shape (n_samples,)
          The predicted data.
      """
      T = column_or_1d(T)
      return 1.0 / (1.0 + np.exp(self.a_ * T + self.b_))
  
imports: from __future__ import division
  import warnings
  from math import log
  import numpy
  from scipy.optimize import fmin_bfgs
  from sklearn.preprocessing import LabelEncoder
  from .base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
  from .preprocessing import label_binarize, LabelBinarizer
  from .utils import check_X_y, check_array, indexable, column_or_1d
  from .utils.validation import check_is_fitted
  from .utils.fixes import signature
  from .isotonic import IsotonicRegression
  from .svm import LinearSVC
  from .model_selection import check_cv
  from .metrics.classification import _check_binary_probabilistic_predictions
label_desc: Use image node_id 2 for calling the _SigmoidCalibration obj's underlying member method code with example usage: obj.predict(T) and returns: unknown