experimental/no_u_turn_sampler/logistic_regression.py
def covertype():
  """Builds the Covertype data set."""
  import sklearn.datasets  # pylint: disable=g-import-not-at-top
  data = sklearn.datasets.covtype.fetch_covtype()
  features = data.data
  labels = data.target

  # Normalize features and append a column of ones for the intercept.
  features -= features.mean(0)
  features /= features.std(0)
  features = np.hstack([features, np.ones([features.shape[0], 1])])
  features = tf.cast(features, dtype=tf.float32)

  # Binarize outcomes on whether it is a specific category.
  _, counts = np.unique(labels, return_counts=True)
  specific_category = np.argmax(counts)
  labels = (labels == specific_category)
  labels = tf.cast(labels, dtype=tf.int32)
  return features, labels
def covertype():
  """Builds the Covertype data set."""
  import sklearn.datasets  # pylint: disable=g-import-not-at-top
  data = sklearn.datasets.covtype.fetch_covtype()
  features = data.data
  labels = data.target

  # Normalize features and append a column of ones for the intercept.
  features -= features.mean(0)
  features /= features.std(0)
  features = np.hstack([features, np.ones([features.shape[0], 1])])
  features = tf.cast(features, dtype=tf.float32)

  # Binarize outcomes on whether it is a specific category.
  _, counts = np.unique(labels, return_counts=True)
  specific_category = np.argmax(counts)
  labels = (labels == specific_category)
  labels = tf.cast(labels, dtype=tf.int32)
  return features, labels
def covertype ( ) : import sklearn . datasets # pylint: disable=g-import-not-at-top data = sklearn . datasets . covtype . fetch_covtype ( ) features = data . data labels = data . target # Normalize features and append a column of ones for the intercept. features -= features . mean ( 0 ) features /= features . std ( 0 ) features = np . hstack ( [ features , np . ones ( [ features . shape [ 0 ] , 1 ] ) ] ) features = tf . cast ( features , dtype = tf . float32 ) # Binarize outcomes on whether it is a specific category. _ , counts = np . unique ( labels , return_counts = True ) specific_category = np . argmax ( counts ) labels = ( labels == specific_category ) labels = tf . cast ( labels , dtype = tf . int32 ) return features , labels
Builds the Covertype data set.
Builds the Covertype data set .