From fe11c6d6d18ba9f56db3793f669edebe402ff44c Mon Sep 17 00:00:00 2001
From: David Luevano Alvarado <55825613+luevano@users.noreply.github.com>
Date: Thu, 26 Mar 2020 15:17:25 -0700
Subject: Separate gaussian and laplacian kernels
---
ml_exp/kernels.py | 88 +++++++++++++++++++++++++++++++++++++++----------------
1 file changed, 62 insertions(+), 26 deletions(-)
diff --git a/ml_exp/kernels.py b/ml_exp/kernels.py
index 23c72475f..d593d83fd 100644
--- a/ml_exp/kernels.py
+++ b/ml_exp/kernels.py
@@ -20,7 +20,6 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
-# import math
import numpy as np
try:
import tensorflow as tf
@@ -30,17 +29,15 @@ except ImportError:
TF_AV = False
-def laplauss_kernel(X1,
+def gaussian_kernel(X1,
X2,
sigma,
- laplauss='gauss',
use_tf=True):
"""
- Calculates the Lpalacian or Gaussian Kernel.
+ Calculates the Gaussian Kernel.
X1: first representations.
X2: second representations.
sigma: kernel width.
- laplauss: which kernel to calculate.
use_tf: if tensorflow should be used.
"""
# If tf is to be used but couldn't be imported, don't try to use it.
@@ -49,12 +46,7 @@ def laplauss_kernel(X1,
X1_size = X1.shape[0]
X2_size = X2.shape[0]
- if laplauss == 'gauss':
- i_sigma = -0.5 / (sigma**2)
- elif laplauss == 'laplace':
- i_sigma = -0.5 / sigma
- else:
- i_sigma = -0.5 / (sigma**2)
+ i_sigma = -0.5 / (sigma**2)
if use_tf:
if tf.config.experimental.list_physical_devices('GPU'):
@@ -72,15 +64,8 @@ def laplauss_kernel(X1,
else:
norm = tf.norm(X2 - X1[i], axis=-1)
- if laplauss == 'gauss':
- return (i + 1,
- K.write(i, tf.exp(i_sigma * tf.square(norm))))
- elif laplauss == 'laplace':
- return (i + 1,
- K.write(i, tf.exp(i_sigma * norm)))
- else:
- return (i + 1,
- K.write(i, tf.exp(i_sigma * tf.square(norm))))
+ return (i + 1,
+ K.write(i, tf.exp(i_sigma * tf.square(norm))))
K = tf.TensorArray(dtype=tf.float64,
size=X1_size)
@@ -91,17 +76,68 @@ def laplauss_kernel(X1,
raise TypeError('No GPU found, could not create Tensor objects.')
else:
K = np.zeros((X1_size, X2_size), dtype=np.float64)
- # Faster way of calculating the kernel (no numba support).
for i in range(X1_size):
if X2.ndim == 3:
norm = np.linalg.norm(X2 - X1[i], axis=(1, 2))
else:
norm = np.linalg.norm(X2 - X1[i], axis=-1)
- if laplauss == 'gauss':
- K[i, :] = np.exp(i_sigma * np.square(norm))
- elif laplauss == 'laplace':
- K[i, :] = np.exp(i_sigma * norm)
+ K[i, :] = np.exp(i_sigma * np.square(norm))
+
+ return K
+
+
+def laplacian_kernel(X1,
+ X2,
+ sigma,
+ use_tf=True):
+ """
+ Calculates the Laplacian Kernel.
+ X1: first representations.
+ X2: second representations.
+ sigma: kernel width.
+ use_tf: if tensorflow should be used.
+ """
+ # If tf is to be used but couldn't be imported, don't try to use it.
+ if use_tf and not TF_AV:
+ use_tf = False
+
+ X1_size = X1.shape[0]
+ X2_size = X2.shape[0]
+ i_sigma = -0.5 / sigma
+
+ if use_tf:
+ if tf.config.experimental.list_physical_devices('GPU'):
+ with tf.device('GPU:0'):
+ X1 = tf.convert_to_tensor(X1)
+ X2 = tf.convert_to_tensor(X2)
+ X2r = tf.rank(X2)
+
+ def cond(i, _):
+ return tf.less(i, X1_size)
+
+ def body(i, K):
+ if X2r == 3:
+ norm = tf.norm(X2 - X1[i], axis=(1, 2))
+ else:
+ norm = tf.norm(X2 - X1[i], axis=-1)
+
+ return (i + 1,
+ K.write(i, tf.exp(i_sigma * norm)))
+
+ K = tf.TensorArray(dtype=tf.float64,
+ size=X1_size)
+ i_state = (0, K)
+ n, K = tf.while_loop(cond, body, i_state)
+ K = K.stack()
+ else:
+ raise TypeError('No GPU found, could not create Tensor objects.')
+ else:
+ K = np.zeros((X1_size, X2_size), dtype=np.float64)
+ for i in range(X1_size):
+ if X2.ndim == 3:
+ norm = np.linalg.norm(X2 - X1[i], axis=(1, 2))
else:
- K[i, :] = np.exp(i_sigma * np.square(norm))
+ norm = np.linalg.norm(X2 - X1[i], axis=-1)
+ K[i, :] = np.exp(i_sigma * norm)
return K
--
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