Support Vector Machines
February 6, 2018
Python machine learning matplotlibThis post covers the first part of the sixth exercise from Andrew Ng’s Machine Learning Course on Coursera.
Introduction
Support vector machines (SVMs) will be used to build a spam classifier.
Example Dataset 1
Figure 1 depicts a 2D example dataset which can be separated by a linear boundary. In this dataset, the positions of the positive examples (indicated with +
) and the negative examples (indicated with o
) suggest a natural separation indicated by the gap. However, there is an outlier positive example +
on the far left at about (0.1, 4.1), which can affect the SVM decision boundary.
import numpy as np
from scipy.io import loadmat
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
data1 = loadmat('ex6data1.mat')
for k in data1:
print(k)
__header__
__version__
__globals__
X
y
X = data1['X']
y = data1['y']
print("Shapes of X{} and y{}".format(X.shape, y.shape))
# Create a function to plot the data.
def plotData(X, y, title):
"""
Plots the data points with '+' for the positive examples
and 'o' for the negative examples.
Args:
X: array (m, 2)
y: array (m, 1)
title: str
"""
# Find indices of positive and negative examples.
y = y.ravel()
pos = y==1
neg = y==0
# Plot examples.
plt.title(title)
plt.plot(X[:,0][pos], X[:,1][pos], "k+")
plt.plot(X[:,0][neg], X[:,1][neg], "yo")
plt.show()
plotData(X, y, "Figure 1: Example Dataset 1")
Shapes of X(51, 2) and y(51, 1)
Different values of the $C$
parameter with SVMs will be tried. The $C$
parameter is a positive value that controls the penalty for misclassified training examples. A large $C$
parameter tells the SVM to try to classify all the examples correctly. $C$
plays a role similar to $\frac{1}{\lambda}$
, where $\lambda$
is the regularization parameter that is used for logistic regression.
When $C=1$
, the SVM puts the decision boundary in the gap between the two datasets and misclassifies the data point on the far left (Figure 2). When $C=100$
, the SVM classifies every single example correctly, but has a decision boundary that does not appear to be a natural fit for the data (Figure 3).
- Note: Most SVM software packages automatically add the extra feature
$x_0=1$
and automatically take care of learning the intercept term$\theta_0$
. The code should be working with training examples$x\in\mathbb{R}^n$
(rather than$x\in\mathbb{R}^{n+1}$
); for example, in the first example dataset$x\in\mathbb{R}^2$
.
SVM with Gaussian Kernels
SVMs will be used to do non-linear classification with Gaussian kernels on datasets that are not linearly separable.
Gaussian Kernel
To find non-linear decision boundaries with the SVM, first a Gaussian kernel should be implemented. The Gaussian kernel is a similarity function that measures the “distance” between a pair of examples, $\left(x^{(i)},x^{(j)}\right)$
. The Gaussian kernel is also parameterized by a bandwidth parameter, $\sigma$
, which determines how fast the similarity metric decreases (to $0$
) as the examples are further apart.
The code in gaussianKernel
computes the Gaussian kernel between two examples, $\left(x^{(i)},x^{(j)}\right)$
. The Gaussian kernel function is defined as:
$$K_{gaussian}\left(x^{(i)},x^{(j)}\right) = exp\left(- \frac{\|x^{(i)}-x^{(j)}\|^2}{2\sigma^2}\right) = exp\left(- \frac{\sum_{k=1}^{n}\left(x_k^{(i)}-x_k^{(j)}\right)^2}{2\sigma^2}\right) $$
from sklearn import svm
import numpy as np
# Create a function that trains a SVM classifier.
def svmTrain(X, y, C, kernelFunction, tol, max_passes, sigma=None):
"""
Trains a SVM classifier using "svm" from "sklearn"
and returns the trained model.
Args:
X: array (# training examples, # features)
Y: array (# training examples, 1),
contains 1 for positive examples
and 0 for negative examples.
C: float, regularization parameter
kernelFunction: string, specifies the kernel type
to be used in the algorithm. It must
be one of 'linear', 'poly', 'rbf',
'sigmoid', 'precomputed' or a callable.
tol: float, a tolerance value used for determining
equality of floating point numbers.
max_passes: int, controls the number of iterations
over the dataset (without changes to alpha)
before the algorithm quits.
sigma: float
Returns:
The classifier trained model
"""
# Change the shape of y to (# training examples, ),
# a 1d array is expected.
y = y.ravel()
if kernelFunction == 'gaussian':
clf = svm.SVC(C=C, kernel='precomputed', tol=tol, max_iter=max_passes)
return clf.fit(gaussianKernel(X, X, sigma=sigma), y)
else:
clf = svm.SVC(C=C, kernel=kernelFunction, tol=tol, max_iter=max_passes)
return clf.fit(X, y)
# Create a funtion that computes the Gaussian kernel between two examples.
def gaussianKernel(X1, X2, sigma):
"""
Returns a gaussian kernel between X1 and X2
and returns the similarity matrix in sim.
Define the kernel by either giving the kernel
as a python function or by precomputing the Gram
matrix.
Args:
X1: array
X2: array
sigma: float, the bandwidth parameter
Returns:
Gram: array
"""
Gram = np.zeros((X1.shape[0], X2.shape[0]))
for i, x1 in enumerate(X1):
for j, x2 in enumerate(X2):
x1 = x1.ravel()
x2 = x2.ravel()
Gram[i, j] = np.exp(-np.sum(np.square(x1 - x2)) / (2 * (sigma**2)))
return Gram
# def gaussianKernel(x1, x2, sigma):
# """
# Returns a gaussian kernel between x1 and x2
# and returns the similarity value in sim.
# Args:
# x1: vector 1d array
# x2: vector 1d array
# sigma: float, the bandwidth parameter
# Returns:
# sim: float
# """
# # Ensure that x1 and x2 are column vectors
# x1 = x1.ravel()
# x2 = x2.ravel()
# sim = np.exp(-np.sum(np.square(x1 - x2)) / (2 * (sigma**2)))
# return sim
# Create a funtion that plots a linear decision boundary.
def visualizeBoundaryLinear(X, y, model, title):
"""
Plots a linear decision boundary learned
by the SVM and overlays the data on it.
Args:
X: array (# training examples, # features)
y: array (# training examples, 1),
contains 1 for positive examples
and 0 for negative examples.
model: the classifier trained model from 'svmTrain()'
title: str
"""
# Get the separating hyperplane.
w = model.coef_[0]
a = -w[0] / w[1]
# Only 2 points are required to define a line, e.g. min and max.
xx = np.array([X[:,0].min(), X[:,0].max()])
yy = a * xx - (model.intercept_[0]) / w[1]
# Plot the separating line.
plt.plot(xx, yy, 'b-')
# Plot the training data.
plotData(X, y, title)
# Change the C value below and see how the decision boundary varies.
C = 1
model = svmTrain(X, y, C, 'linear', 1e-3, -1)
visualizeBoundaryLinear(X, y, model, "Figure 2: SVM Decision Boundary with C = 1")
C = 100
model = svmTrain(X, y, C, 'linear', 1e-3, -1)
visualizeBoundaryLinear(X, y, model, "Figure 3: SVM Decision Boundary with C = 100")
print("Evaluating the Gaussian Kernel ...")
x1 = np.array([1, 2, 1])
x2 = np.array([0, 4, -1])
sigma = 2
sim = gaussianKernel(x1, x2, sigma)
print("Gaussian Kernel between x1= [1, 2, 1], x2= [0, 4, -1], sigma= 2: \n{}".format(sim))
Evaluating the Gaussian Kernel ...
Gaussian Kernel between x1= [1, 2, 1], x2= [0, 4, -1], sigma= 2:
[[ 0.8824969 0.32465247 0.60653066]
[ 0.60653066 0.60653066 0.32465247]
[ 0.8824969 0.32465247 0.60653066]]
Example Dataset 2
Figure 4 shows the dataset 2. From the figure, it can be observed that there is no linear decision boundary that separates the positive and negative examples for this dataset. However, by using the Gaussian
kernel with the SVM, a non-linear decision boundary will be learnt, that can perform reasonably well for the dataset.
data2 = loadmat('ex6data2.mat')
X = data2['X']
y = data2['y']
print("Shapes of X{} and y{}".format(X.shape, y.shape))
plotData(X, y, "Figure 4: Example Dataset 2")
Shapes of X(863, 2) and y(863, 1)
Figure 5 shows the decision boundary found by the SVM with a Gaussian kernel. The decision boundary is able to separate most of the positive and negative examples correctly and follows the contours of the dataset well.
# Create a funtion that plots a non-linear decision boundary.
def visualizeBoundary(X, y, model, title):
"""
Plots a non-linear decision boundary learned
by the SVM and overlays the data on it.
Args:
X: array (# training examples, # features)
y: array (# training examples, 1),
contains 1 for positive examples
and 0 for negative examples.
model: the classifier trained model from 'svmTrain()'
title: str
"""
# Make classification predictions over a grid of values.
x1plot = np.linspace(X[:,0].min(), X[:,0].max(), 100).T
x2plot = np.linspace(X[:,1].min(), X[:,1].max(), 100).T
X1, X2 = np.meshgrid(x1plot, x2plot)
vals = np.zeros(X1.shape)
for i in range(X1.shape[1]):
this_X = np.column_stack((X1[:, i], X2[:, i]))
vals[:, i] = model.predict(gaussianKernel(this_X, X, sigma))
# Plot the SVM boundary
plt.contour(X1, X2, vals, colors="b", levels=[0,0])
# Plot the training data.
plotData(X, y, title)
print("Training SVM with RBF Kernel (this may take 1 to 2 minutes)...")
# Set SVM parameters.
C = 1
sigma = 0.1
# We set the tolerance and max_passes lower here so that the code will run
# faster. However, in practice, you will want to run the training to convergence.
model = svmTrain(X, y, C, 'gaussian', 1e-3, 100, sigma)
visualizeBoundary(X, y, model, "Figure 5: SVM (Gaussian Kernel) Decision Boundary")
Training SVM with RBF Kernel (this may take 1 to 2 minutes)...
Example Dataset 3
Figure 6 displays a third dataset. Again the SVM with the Gaussian kernel will be used with this dataset.
In the provided dataset, ex6data3.mat, the variables $X$
, $y$
, $Xval$
, $yval$
are given. The SVM classifier is trained using the training set $(X, y)$
using parameters loaded from dataset3Params
.
data3 = loadmat('ex6data3.mat')
X = data3['X']
y = data3['y']
Xval = data3['Xval']
yval = data3['yval']
print("Shapes of X{} and y{}".format(X.shape, y.shape))
plotData(X, y, "Figure 6: Example Dataset 3")
Shapes of X(211, 2) and y(211, 1)
Using the cross validation set $Xval$
, $yval$
, the best $C$
and $\sigma$
parameter to use will be determined. Additional necessary code helps to search over the parameters $C$
and $\sigma$
. For both $C$
and $\sigma$
, it is suggested trying values in multiplicative steps $\left(e.g., 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30\right)$
. Note that all possible pairs of values for $C$
and $\sigma$
should be tried $\left(e.g., C = 0.3 \text{ and } \sigma = 0.1\right)$
. For example, for the 8 values of $C$
and $\sigma^2$
listed above, the training and evaluating (on the cross validation set) combinations of different models are $8^2 = 64$
.
After determining the best $C$
and $\sigma$
parameters, the code in dataset3Params
is modified, filling in the best parameters that were found. For the best parameters, the SVM returns a decision boundary shown in Figure 7.
- Tip: When implementing cross validation to select the best
$C$
and$\sigma$
parameter to use, the error must be evaluated on the cross validation set. Recall that for classification, the error is defined as the fraction of the cross validation examples that were classified incorrectly. This error can be computed usingnp.mean((predictions != yval).astype(int))
, where predictions is a vector containing all the predictions from the SVM, andyval
are the true labels from the cross validation set.
# Create a function to find the optimal parameters C and sigma.
def dataset3Params(X, y, Xval, yval):
"""
Returns the choice of optimal C and sigma learning parameters -
based on a cross-validation set - to use for SVM with RBF kernel.
"""
predictions = dict()
for C in [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]:
for sigma in [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]:
# Train the model on X and y.
model = svmTrain(X, y, C, 'gaussian', 1e-5, -1, sigma)
# Perform classification on samples in Xval.
# For precomputed kernels, the expected shape of
# X is [n_samples_validation, n_samples_train]
prediction = model.predict(gaussianKernel(Xval, X, sigma))
# Compute the prediction errors.
predictions[(C, sigma)] = np.mean((prediction != yval).astype(int))
C, sigma = min(predictions, key=predictions.get)
return C, sigma
# Find the optimal C and sigma.
C, sigma = dataset3Params(X, y, Xval, yval)
# Train the SVM classifier with the optimal values of C and sigma.
model = svmTrain(X, y, C, 'gaussian', 1e-5, -1, sigma=sigma)
# Plot the data and the decision boundary.
visualizeBoundary(X, y, model, sigma, "Figure 7: SVM (Gaussian Kernel) Decision Boundary")


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In hypothesis testing, rejecting the null hypothesis typically means we found something interesting — a drug performs better than the placebo, there is a difference between two fertilizers, the new textbook helps students achieve better test scores than the current one, etc. Rejecting the null hypothesis when it is true amounts to a false discovery. On the other hand, accepting the null hypothesis when it is false means you failed to find an interesting result. Here are some common synonyms:
Performing multiple hypothesis tests inflates false positive rates. Imagine 500 science labs around the world try to determine if playing music to pea plants makes them grow faster. Each lab uses the same experiment design and performs a t-test with significance level . Assuming the null that music has no effect on pea growth, each lab will reject the null with probability . Although each individual lab is unlikely to reject the null, there is a greater than chance that some lab will reject the null:
There are a couple ways to address this inflation of false positives:
For hypothesis tests, the family-wise error rate is the false positive rate for the family of tests: the probability that one of the tests rejects the null when it is true. To control the FWER at , we set the significance level for each individual test at . Alternatively, we can use significance level for each individual test, but form Bonferroni-adjusted -values by multiplying each -value by . The Bonferroni correction only works for a moderate number of tests — if is too large, each individual test will be too cautious and never reject the null. You’ll never find anything interesting!
A more modern approach for multiple hypothesis testing is to control the false-discovery rate . We accept that some hypothesis tests will result in false positives, but control the fraction of false-positives to all positives. The test works as follows.
Choose a false-discovery rate , and compute the q-values . Look at the ordered -values corresponding to (after relabeling) the null hypotheses , and find the largest index with for . Reject the first hypotheses . This procedure caps the FDR at , and unlike the Bonferroni correction, we will typically reject some hypotheses.
Written on April 13th, 2018 by Scott Roy


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--- layout: post title: "Migration to Jekyll" description: "" comments: true category: blog tags: [migration, blog, posts, import] --- {% include JB/setup %}
I wan't to start writting some blog posts so I've thought that I will start from migration of My old blog on Blogger (http://blog.wysocki.org.pl) to new and shiny Jekyll powered blog engine (http://exu.github.io) - domain will be changed later. Why? It looks like good idea to have everything in git. And what is most important I can write my posts in Org-mode (markdown sucks so much!)
Config
to RbConfig
because of deprecation warning)'
_org
directory in jekyll root directory structure similar to root directory.
I have _posts
directory with org files inside
To avoid <p>
tags in jekyll parameters I've put them between #+BEGIN_HTML
and #+END_HTML
tags
#+STARTUP: showall indent #+STARTUP: hidestars #+OPTIONS: H:4 num:nil tags:nil toc:nil timestamps:t #+BEGIN_HTML --- layout: post title: "Migration to Jekyll" description: "" comments: true category: blog tags: [migration, blog, posts, import] --- {% include JB/setup %} #+END_HTML
I've added some little helper to publish org files by hitting one key:
(setq org-publish-project-alist '( ("github" ;; Path to your org files. :base-directory "~/Workspace/exu.github.com/_org/" :base-extension "org" ;; Path to your Jekyll project. :publishing-directory "~/Workspace/exu.github.com/" :recursive t :publishing-function org-publish-org-to-html :headline-levels 4 :html-extension "html" :body-only t ;; Only export section between <body> </body> ) )) (defun publish-blog () (interactive) (org-publish "github") ) (global-set-key (kbd "<f12>") 'publish-blog)
[3/6]
:[X]
Make new home page
[X]
Integrate Org-mode to work on github pages
[ ]
Add some static pages with informations
[ ]
Migrate domain: blog.wysocki.org.pl
[ ]
Make some Bootstrap layout
[X]
Find some nice font


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bitwise_not
(in
octant.ocean.time
)
bitwise_xor
(in
octant.ocean.time
)
beta()
(in
octant.sw.density
)
bitwise_not
(in
octant.ocean.velocity
)
bitwise_xor
(in
octant.ocean.velocity
)
bitwise_and
(in
octant.ocean
)
bitwise_or
(in
octant.ocean
)
BoundaryInteractor
(in
octant.grid
)
bitwise_and
(in
octant.ocean.depths
)
bitwise_or
(in
octant.ocean.depths
)
BUFSIZE
(in
octant.ocean
)
bitwise_and
(in
octant.ocean.state
)
bitwise_or
(in
octant.ocean.state
)
BUFSIZE
(in
octant.ocean.depths
)
bitwise_and
(in
octant.ocean.step3d_t
)
bitwise_or
(in
octant.ocean.step3d_t
)
BUFSIZE
(in
octant.ocean.state
)
bitwise_and
(in
octant.ocean.time
)
bitwise_or
(in
octant.ocean.time
)
BUFSIZE
(in
octant.ocean.step3d_t
)
bitwise_and
(in
octant.ocean.velocity
)
bitwise_or
(in
octant.ocean.velocity
)
BUFSIZE
(in
octant.ocean.time
)
bitwise_not
(in
octant.ocean
)
bitwise_xor
(in
octant.ocean
)
BUFSIZE
(in
octant.ocean.velocity
)
bitwise_not
(in
octant.ocean.depths
)
bitwise_xor
(in
octant.ocean.depths
)
C
c_
(in
octant.ocean
)
CGrid_geo
(in
octant.grid
)
conjugate
(in
octant.ocean.depths
)
c_
(in
octant.ocean.depths
)
CLIP
(in
octant.ocean
)
conjugate
(in
octant.ocean.state
)
c_
(in
octant.ocean.state
)
CLIP
(in
octant.ocean.depths
)
conjugate
(in
octant.ocean.step3d_t
)
c_
(in
octant.ocean.step3d_t
)
CLIP
(in
octant.ocean.state
)
conjugate
(in
octant.ocean.time
)
c_
(in
octant.ocean.time
)
CLIP
(in
octant.ocean.step3d_t
)
conjugate
(in
octant.ocean.velocity
)
c_
(in
octant.ocean.velocity
)
CLIP
(in
octant.ocean.time
)
cos
(in
octant.ocean
)
calculate_orthogonality()
(in
CGrid
)
CLIP
(in
octant.ocean.velocity
)
cos
(in
octant.ocean.depths
)
cast
(in
octant.ocean
)
close()
(in
NetCDFFile
)
cos
(in
octant.ocean.state
)
cast
(in
octant.ocean.depths
)
close()
(in
Bdy2d
)
cos
(in
octant.ocean.step3d_t
)
cast
(in
octant.ocean.state
)
close()
(in
Bdy3d
)
cos
(in
octant.ocean.time
)
cast
(in
octant.ocean.step3d_t
)
cmap_brightened()
(in
octant.plotting
)
cos
(in
octant.ocean.velocity
)
cast
(in
octant.ocean.time
)
cmap_discretize()
(in
octant.plotting
)
cosh
(in
octant.ocean
)
cast
(in
octant.ocean.velocity
)
cmap_map()
(in
octant.plotting
)
cosh
(in
octant.ocean.depths
)
ceil
(in
octant.ocean
)
cond()
(in
octant.sw.salinity
)
cosh
(in
octant.ocean.state
)
ceil
(in
octant.ocean.depths
)
conj
(in
octant.ocean
)
cosh
(in
octant.ocean.step3d_t
)
ceil
(in
octant.ocean.state
)
conj
(in
octant.ocean.depths
)
cosh
(in
octant.ocean.time
)
ceil
(in
octant.ocean.step3d_t
)
conj
(in
octant.ocean.state
)
cosh
(in
octant.ocean.velocity
)
ceil
(in
octant.ocean.time
)
conj
(in
octant.ocean.step3d_t
)
count_neighbors()
(in
KDTree
)
ceil
(in
octant.ocean.velocity
)
conj
(in
octant.ocean.time
)
Cs
(in
s_coordinate
)
cf
(in
octant
)
conj
(in
octant.ocean.velocity
)
Cs
(in
Depths
)
CGrid
(in
octant.grid
)
conjugate
(in
octant.ocean
)
D
Dataset()
(in
octant.io
)
degrees
(in
octant.ocean
)
Depths
(in
octant.ocean.depths
)
dates
(in
time
)
degrees
(in
octant.ocean.depths
)
distance_matrix()
(in
octant.extern.kdtree
)
dates
(in
time
)
degrees
(in
octant.ocean.state
)
divide
(in
octant.ocean.depths
)
days
(in
time
)
degrees
(in
octant.ocean.step3d_t
)
divide
(in
octant.ocean
)
days
(in
time
)
degrees
(in
octant.ocean.time
)
divide
(in
octant.ocean.state
)
deg2rad
(in
octant.ocean
)
degrees
(in
octant.ocean.velocity
)
divide
(in
octant.ocean.step3d_t
)
deg2rad
(in
octant.ocean.depths
)
dens()
(in
octant.sw.density
)
divide
(in
octant.ocean.time
)
deg2rad
(in
octant.ocean.state
)
density
(in
octant.sw
)
divide
(in
octant.ocean.velocity
)
deg2rad
(in
octant.ocean.step3d_t
)
depth()
(in
octant.sw.misc
)
drawscale()
(in
octant.plotting
)
deg2rad
(in
octant.ocean.time
)
depths
(in
octant
)
drhods()
(in
octant.sw.density
)
deg2rad
(in
octant.ocean.velocity
)
depths
(in
octant.ocean
)
drhodt()
(in
octant.sw.density
)
E
e
(in
octant.ocean.depths
)
ERR_DEFAULT2
(in
octant.ocean.step3d_t
)
ERR_WARN
(in
octant.ocean.depths
)
e
(in
octant.ocean
)
ERR_DEFAULT2
(in
octant.ocean.time
)
ERR_WARN
(in
octant.ocean.state
)
e
(in
octant.ocean.state
)
ERR_DEFAULT2
(in
octant.ocean.velocity
)
ERR_WARN
(in
octant.ocean.step3d_t
)
e
(in
octant.ocean.step3d_t
)
ERR_IGNORE
(in
octant.ocean
)
ERR_WARN
(in
octant.ocean.time
)
e
(in
octant.ocean.time
)
ERR_IGNORE
(in
octant.ocean.depths
)
ERR_WARN
(in
octant.ocean.velocity
)
e
(in
octant.ocean.velocity
)
ERR_IGNORE
(in
octant.ocean.state
)
exp
(in
octant.ocean.depths
)
edit_mask_mesh
(in
octant.grid
)
ERR_IGNORE
(in
octant.ocean.step3d_t
)
exp
(in
octant.ocean
)
equal
(in
octant.ocean.depths
)
ERR_IGNORE
(in
octant.ocean.time
)
exp
(in
octant.ocean.state
)
equal
(in
octant.ocean
)
ERR_IGNORE
(in
octant.ocean.velocity
)
exp
(in
octant.ocean.step3d_t
)
equal
(in
octant.ocean.state
)
ERR_LOG
(in
octant.ocean
)
exp
(in
octant.ocean.time
)
equal
(in
octant.ocean.step3d_t
)
ERR_LOG
(in
octant.ocean.depths
)
exp
(in
octant.ocean.velocity
)
equal
(in
octant.ocean.time
)
ERR_LOG
(in
octant.ocean.state
)
exp2
(in
octant.ocean.depths
)
equal
(in
octant.ocean.velocity
)
ERR_LOG
(in
octant.ocean.step3d_t
)
exp2
(in
octant.ocean
)
ERR_CALL
(in
octant.ocean
)
ERR_LOG
(in
octant.ocean.time
)
exp2
(in
octant.ocean.state
)
ERR_CALL
(in
octant.ocean.depths
)
ERR_LOG
(in
octant.ocean.velocity
)
exp2
(in
octant.ocean.step3d_t
)
ERR_CALL
(in
octant.ocean.state
)
ERR_PRINT
(in
octant.ocean
)
exp2
(in
octant.ocean.time
)
ERR_CALL
(in
octant.ocean.step3d_t
)
ERR_PRINT
(in
octant.ocean.depths
)
exp2
(in
octant.ocean.velocity
)
ERR_CALL
(in
octant.ocean.time
)
ERR_PRINT
(in
octant.ocean.state
)
expm1
(in
octant.ocean.depths
)
ERR_CALL
(in
octant.ocean.velocity
)
ERR_PRINT
(in
octant.ocean.step3d_t
)
expm1
(in
octant.ocean
)
ERR_DEFAULT
(in
octant.ocean
)
ERR_PRINT
(in
octant.ocean.time
)
expm1
(in
octant.ocean.state
)
ERR_DEFAULT
(in
octant.ocean.depths
)
ERR_PRINT
(in
octant.ocean.velocity
)
expm1
(in
octant.ocean.step3d_t
)
ERR_DEFAULT
(in
octant.ocean.state
)
ERR_RAISE
(in
octant.ocean
)
expm1
(in
octant.ocean.time
)
ERR_DEFAULT
(in
octant.ocean.step3d_t
)
ERR_RAISE
(in
octant.ocean.depths
)
expm1
(in
octant.ocean.velocity
)
ERR_DEFAULT
(in
octant.ocean.time
)
ERR_RAISE
(in
octant.ocean.state
)
extern
(in
octant
)
ERR_DEFAULT
(in
octant.ocean.velocity
)
ERR_RAISE
(in
octant.ocean.step3d_t
)
extrapolate()
(in
Transect_extrapolator
)
ERR_DEFAULT2
(in
octant.ocean
)
ERR_RAISE
(in
octant.ocean.time
)
extrapolate_mask()
(in
octant.tools
)
ERR_DEFAULT2
(in
octant.ocean.depths
)
ERR_RAISE
(in
octant.ocean.velocity
)
ERR_DEFAULT2
(in
octant.ocean.state
)
ERR_WARN
(in
octant.ocean
)
F
fabs
(in
octant.ocean.depths
)
floor_divide
(in
octant.ocean.state
)
FPE_DIVIDEBYZERO
(in
octant.ocean.time
)
fabs
(in
octant.ocean
)
floor_divide
(in
octant.ocean.step3d_t
)
FPE_DIVIDEBYZERO
(in
octant.ocean.velocity
)
fabs
(in
octant.ocean.state
)
floor_divide
(in
octant.ocean.time
)
FPE_INVALID
(in
octant.ocean
)
fabs
(in
octant.ocean.step3d_t
)
floor_divide
(in
octant.ocean.velocity
)
FPE_INVALID
(in
octant.ocean.depths
)
fabs
(in
octant.ocean.time
)
fmax
(in
octant.ocean.depths
)
FPE_INVALID
(in
octant.ocean.state
)
fabs
(in
octant.ocean.velocity
)
fmax
(in
octant.ocean
)
FPE_INVALID
(in
octant.ocean.step3d_t
)
False_
(in
octant.ocean
)
fmax
(in
octant.ocean.state
)
FPE_INVALID
(in
octant.ocean.time
)
False_
(in
octant.ocean.depths
)
fmax
(in
octant.ocean.step3d_t
)
FPE_INVALID
(in
octant.ocean.velocity
)
False_
(in
octant.ocean.state
)
fmax
(in
octant.ocean.time
)
FPE_OVERFLOW
(in
octant.ocean
)
False_
(in
octant.ocean.step3d_t
)
fmax
(in
octant.ocean.velocity
)
FPE_OVERFLOW
(in
octant.ocean.depths
)
False_
(in
octant.ocean.time
)
fmin
(in
octant.ocean.depths
)
FPE_OVERFLOW
(in
octant.ocean.state
)
False_
(in
octant.ocean.velocity
)
fmin
(in
octant.ocean
)
FPE_OVERFLOW
(in
octant.ocean.step3d_t
)
filename
(in
Bdyinfo
)
fmin
(in
octant.ocean.state
)
FPE_OVERFLOW
(in
octant.ocean.time
)
FLOATING_POINT_SUPPORT
(in
octant.ocean
)
fmin
(in
octant.ocean.step3d_t
)
FPE_OVERFLOW
(in
octant.ocean.velocity
)
FLOATING_POINT_SUPPORT
(in
octant.ocean.depths
)
fmin
(in
octant.ocean.time
)
FPE_UNDERFLOW
(in
octant.ocean
)
FLOATING_POINT_SUPPORT
(in
octant.ocean.state
)
fmin
(in
octant.ocean.velocity
)
FPE_UNDERFLOW
(in
octant.ocean.depths
)
FLOATING_POINT_SUPPORT
(in
octant.ocean.step3d_t
)
fmod
(in
octant.ocean.depths
)
FPE_UNDERFLOW
(in
octant.ocean.state
)
FLOATING_POINT_SUPPORT
(in
octant.ocean.time
)
fmod
(in
octant.ocean
)
FPE_UNDERFLOW
(in
octant.ocean.step3d_t
)
FLOATING_POINT_SUPPORT
(in
octant.ocean.velocity
)
fmod
(in
octant.ocean.state
)
FPE_UNDERFLOW
(in
octant.ocean.time
)
floor
(in
octant.ocean.depths
)
fmod
(in
octant.ocean.step3d_t
)
FPE_UNDERFLOW
(in
octant.ocean.velocity
)
floor
(in
octant.ocean
)
fmod
(in
octant.ocean.time
)
freezept()
(in
octant.sw.misc
)
floor
(in
octant.ocean.state
)
fmod
(in
octant.ocean.velocity
)
frexp
(in
octant.ocean.depths
)
floor
(in
octant.ocean.step3d_t
)
Focus
(in
octant.grid
)
frexp
(in
octant.ocean
)
floor
(in
octant.ocean.time
)
FPE_DIVIDEBYZERO
(in
octant.ocean
)
frexp
(in
octant.ocean.state
)
floor
(in
octant.ocean.velocity
)
FPE_DIVIDEBYZERO
(in
octant.ocean.depths
)
frexp
(in
octant.ocean.step3d_t
)
floor_divide
(in
octant.ocean.depths
)
FPE_DIVIDEBYZERO
(in
octant.ocean.state
)
frexp
(in
octant.ocean.time
)
floor_divide
(in
octant.ocean
)
FPE_DIVIDEBYZERO
(in
octant.ocean.step3d_t
)
frexp
(in
octant.ocean.velocity
)
G
gc_dist
(in
octant.tools
)
get_nbdy()
(in
Bdy2d
)
greater
(in
octant.ocean
)
generate_grid()
(in
Gridgen
)
get_nbdy()
(in
Bdy3d
)
greater
(in
octant.ocean.state
)
get_dates()
(in
time
)
get_seconds()
(in
time
)
greater
(in
octant.ocean.step3d_t
)
get_dates()
(in
time
)
get_seconds()
(in
time
)
greater
(in
octant.ocean.time
)
get_days()
(in
time
)
get_tnum()
(in
Bdy2d
)
greater
(in
octant.ocean.velocity
)
get_days()
(in
time
)
get_tnum()
(in
Bdy3d
)
greater_equal
(in
octant.ocean.depths
)
get_filename()
(in
Bdyinfo
)
get_xdata()
(in
BoundaryInteractor
)
greater_equal
(in
octant.ocean
)
get_hours()
(in
time
)
get_ydata()
(in
BoundaryInteractor
)
greater_equal
(in
octant.ocean.state
)
get_hours()
(in
time
)
get_znum()
(in
Bdy3d
)
greater_equal
(in
octant.ocean.step3d_t
)
get_indlist()
(in
Bdyinfo
)
getm
(in
octant
)
greater_equal
(in
octant.ocean.time
)
get_jd()
(in
time
)
getValue()
(in
NetCDFVariable
)
greater_equal
(in
octant.ocean.velocity
)
get_jd()
(in
time
)
greatcircle
(in
octant.extern
)
grid
(in
octant
)
get_minutes()
(in
time
)
GreatCircle
(in
octant.extern.greatcircle
)
Gridgen
(in
octant.grid
)
get_minutes()
(in
time
)
greater
(in
octant.ocean.depths
)
H
heat
(in
octant.sw
)
hypot
(in
octant.ocean.depths
)
hypot
(in
octant.ocean.time
)
heatcap()
(in
octant.sw.heat
)
hypot
(in
octant.ocean
)
hypot
(in
octant.ocean.velocity
)
hours
(in
time
)
hypot
(in
octant.ocean.state
)
hours
(in
time
)
hypot
(in
octant.ocean.step3d_t
)
I
index_exp
(in
octant.ocean.depths
)
Infinity
(in
octant.ocean.state
)
isfinite
(in
octant.ocean.step3d_t
)
index_exp
(in
octant.ocean
)
Infinity
(in
octant.ocean.step3d_t
)
isfinite
(in
octant.ocean.time
)
index_exp
(in
octant.ocean.state
)
Infinity
(in
octant.ocean.time
)
isfinite
(in
octant.ocean.velocity
)
index_exp
(in
octant.ocean.step3d_t
)
Infinity
(in
octant.ocean.velocity
)
isinf
(in
octant.ocean.depths
)
index_exp
(in
octant.ocean.time
)
infty
(in
octant.ocean.depths
)
isinf
(in
octant.ocean
)
index_exp
(in
octant.ocean.velocity
)
infty
(in
octant.ocean
)
isinf
(in
octant.ocean.state
)
indlist
(in
Bdyinfo
)
infty
(in
octant.ocean.state
)
isinf
(in
octant.ocean.step3d_t
)
Inf
(in
octant.ocean
)
infty
(in
octant.ocean.step3d_t
)
isinf
(in
octant.ocean.time
)
Inf
(in
octant.ocean.depths
)
infty
(in
octant.ocean.time
)
isinf
(in
octant.ocean.velocity
)
inf
(in
octant.ocean.depths
)
infty
(in
octant.ocean.velocity
)
isnan
(in
octant.ocean.depths
)
inf
(in
octant.ocean
)
innernode
(in
KDTree
)
isnan
(in
octant.ocean
)
Inf
(in
octant.ocean.state
)
invert
(in
octant.ocean.depths
)
isnan
(in
octant.ocean.state
)
inf
(in
octant.ocean.state
)
invert
(in
octant.ocean
)
isnan
(in
octant.ocean.step3d_t
)
Inf
(in
octant.ocean.step3d_t
)
invert
(in
octant.ocean.state
)
isnan
(in
octant.ocean.time
)
inf
(in
octant.ocean.step3d_t
)
invert
(in
octant.ocean.step3d_t
)
isnan
(in
octant.ocean.velocity
)
Inf
(in
octant.ocean.time
)
invert
(in
octant.ocean.time
)
iso_integrate()
(in
octant.slice
)
inf
(in
octant.ocean.time
)
invert
(in
octant.ocean.velocity
)
iso_integrate()
(in
octant.tools
)
Inf
(in
octant.ocean.velocity
)
io
(in
octant
)
isoslice()
(in
octant.slice
)
inf
(in
octant.ocean.velocity
)
isfinite
(in
octant.ocean.depths
)
isoslice()
(in
octant.tools
)
Infinity
(in
octant.ocean
)
isfinite
(in
octant.ocean
)
Infinity
(in
octant.ocean.depths
)
isfinite
(in
octant.ocean.state
)
J
jd
(in
time
)
jd
(in
time
)
K
kdtree
(in
octant.extern
)
KDTree
(in
octant.extern.kdtree
)
L
lat
(in
CGrid_geo
)
little_endian
(in
octant.ocean.step3d_t
)
logaddexp2
(in
octant.ocean.state
)
layers()
(in
octant.plotting
)
little_endian
(in
octant.ocean.time
)
logaddexp2
(in
octant.ocean.step3d_t
)
ldexp
(in
octant.ocean.depths
)
little_endian
(in
octant.ocean.velocity
)
logaddexp2
(in
octant.ocean.time
)
ldexp
(in
octant.ocean
)
load_bry()
(in
BoundaryInteractor
)
logaddexp2
(in
octant.ocean.velocity
)
ldexp
(in
octant.ocean.state
)
log
(in
octant.ocean.depths
)
logical_and
(in
octant.ocean.depths
)
ldexp
(in
octant.ocean.step3d_t
)
log
(in
octant.ocean
)
logical_and
(in
octant.ocean
)
ldexp
(in
octant.ocean.time
)
log
(in
octant.ocean.state
)
logical_and
(in
octant.ocean.state
)
ldexp
(in
octant.ocean.velocity
)
log
(in
octant.ocean.step3d_t
)
logical_and
(in
octant.ocean.step3d_t
)
leafnode
(in
KDTree
)
log
(in
octant.ocean.time
)
logical_and
(in
octant.ocean.time
)
left_shift
(in
octant.ocean.depths
)
log
(in
octant.ocean.velocity
)
logical_and
(in
octant.ocean.velocity
)
left_shift
(in
octant.ocean
)
log10
(in
octant.ocean.depths
)
logical_not
(in
octant.ocean.depths
)
left_shift
(in
octant.ocean.state
)
log10
(in
octant.ocean
)
logical_not
(in
octant.ocean
)
left_shift
(in
octant.ocean.step3d_t
)
log10
(in
octant.ocean.state
)
logical_not
(in
octant.ocean.state
)
left_shift
(in
octant.ocean.time
)
log10
(in
octant.ocean.step3d_t
)
logical_not
(in
octant.ocean.step3d_t
)
left_shift
(in
octant.ocean.velocity
)
log10
(in
octant.ocean.time
)
logical_not
(in
octant.ocean.time
)
less
(in
octant.ocean.depths
)
log10
(in
octant.ocean.velocity
)
logical_not
(in
octant.ocean.velocity
)
less
(in
octant.ocean
)
log1p
(in
octant.ocean.depths
)
logical_or
(in
octant.ocean.depths
)
less
(in
octant.ocean.state
)
log1p
(in
octant.ocean
)
logical_or
(in
octant.ocean
)
less
(in
octant.ocean.step3d_t
)
log1p
(in
octant.ocean.state
)
logical_or
(in
octant.ocean.state
)
less
(in
octant.ocean.time
)
log1p
(in
octant.ocean.step3d_t
)
logical_or
(in
octant.ocean.step3d_t
)
less
(in
octant.ocean.velocity
)
log1p
(in
octant.ocean.time
)
logical_or
(in
octant.ocean.time
)
less_equal
(in
octant.ocean.depths
)
log1p
(in
octant.ocean.velocity
)
logical_or
(in
octant.ocean.velocity
)
less_equal
(in
octant.ocean
)
logaddexp
(in
octant.ocean.depths
)
logical_xor
(in
octant.ocean.depths
)
less_equal
(in
octant.ocean.state
)
logaddexp
(in
octant.ocean
)
logical_xor
(in
octant.ocean
)
less_equal
(in
octant.ocean.step3d_t
)
logaddexp
(in
octant.ocean.state
)
logical_xor
(in
octant.ocean.state
)
less_equal
(in
octant.ocean.time
)
logaddexp
(in
octant.ocean.step3d_t
)
logical_xor
(in
octant.ocean.step3d_t
)
less_equal
(in
octant.ocean.velocity
)
logaddexp
(in
octant.ocean.time
)
logical_xor
(in
octant.ocean.time
)
little_endian
(in
octant.ocean.depths
)
logaddexp
(in
octant.ocean.velocity
)
logical_xor
(in
octant.ocean.velocity
)
little_endian
(in
octant.ocean
)
logaddexp2
(in
octant.ocean.depths
)
lon
(in
CGrid_geo
)
little_endian
(in
octant.ocean.state
)
logaddexp2
(in
octant.ocean
)
M
mask
(in
CGrid
)
MFDataset()
(in
octant.io
)
mod
(in
octant.ocean.depths
)
mask_polygon()
(in
CGrid
)
mgrid
(in
octant.ocean.depths
)
mod
(in
octant.ocean
)
mask_polygon_geo()
(in
CGrid_geo
)
mgrid
(in
octant.ocean
)
mod
(in
octant.ocean.state
)
mask_psi
(in
CGrid
)
mgrid
(in
octant.ocean.state
)
mod
(in
octant.ocean.step3d_t
)
mask_u
(in
CGrid
)
mgrid
(in
octant.ocean.step3d_t
)
mod
(in
octant.ocean.time
)
mask_v
(in
CGrid
)
mgrid
(in
octant.ocean.time
)
mod
(in
octant.ocean.velocity
)
max_distance_point()
(in
Rectangle
)
mgrid
(in
octant.ocean.velocity
)
modf
(in
octant.ocean.depths
)
max_distance_rectangle()
(in
Rectangle
)
min_distance_point()
(in
Rectangle
)
modf
(in
octant.ocean
)
MAXDIMS
(in
octant.ocean
)
min_distance_rectangle()
(in
Rectangle
)
modf
(in
octant.ocean.state
)
MAXDIMS
(in
octant.ocean.depths
)
minimum
(in
octant.ocean.depths
)
modf
(in
octant.ocean.step3d_t
)
MAXDIMS
(in
octant.ocean.state
)
minimum
(in
octant.ocean
)
modf
(in
octant.ocean.time
)
MAXDIMS
(in
octant.ocean.step3d_t
)
minimum
(in
octant.ocean.state
)
modf
(in
octant.ocean.velocity
)
MAXDIMS
(in
octant.ocean.time
)
minimum
(in
octant.ocean.step3d_t
)
multiply
(in
octant.ocean.depths
)
MAXDIMS
(in
octant.ocean.velocity
)
minimum
(in
octant.ocean.time
)
multiply
(in
octant.ocean
)
maximum
(in
octant.ocean.depths
)
minimum
(in
octant.ocean.velocity
)
multiply
(in
octant.ocean.state
)
maximum
(in
octant.ocean
)
minkowski_distance()
(in
octant.extern.kdtree
)
multiply
(in
octant.ocean.step3d_t
)
maximum
(in
octant.ocean.state
)
minkowski_distance_p()
(in
octant.extern.kdtree
)
multiply
(in
octant.ocean.time
)
maximum
(in
octant.ocean.step3d_t
)
minutes
(in
time
)
multiply
(in
octant.ocean.velocity
)
maximum
(in
octant.ocean.time
)
minutes
(in
time
)
maximum
(in
octant.ocean.velocity
)
misc
(in
octant.sw
)
N
N2()
(in
octant.tools
)
NC_ATTRIBUTE
(in
octant.extern.pupynere
)
NetCDFVariable
(in
octant.extern.pupynere
)
NAN
(in
octant.ocean
)
NC_BYTE
(in
octant.extern.pupynere
)
newaxis
(in
octant.ocean.depths
)
NaN
(in
octant.ocean
)
NC_CHAR
(in
octant.extern.pupynere
)
newaxis
(in
octant.ocean
)
NAN
(in
octant.ocean.depths
)
nc_curl()
(in
octant.roms
)
newaxis
(in
octant.ocean.state
)
NaN
(in
octant.ocean.depths
)
nc_depths
(in
octant.roms
)
newaxis
(in
octant.ocean.step3d_t
)
nan
(in
octant.ocean.depths
)
NC_DIMENSION
(in
octant.extern.pupynere
)
newaxis
(in
octant.ocean.time
)
nan
(in
octant.ocean
)
nc_div()
(in
octant.roms
)
newaxis
(in
octant.ocean.velocity
)
NAN
(in
octant.ocean.state
)
NC_DOUBLE
(in
octant.extern.pupynere
)
NINF
(in
octant.ocean
)
NaN
(in
octant.ocean.state
)
NC_FLOAT
(in
octant.extern.pupynere
)
NINF
(in
octant.ocean.depths
)
nan
(in
octant.ocean.state
)
nc_gls_dissipation()
(in
octant.roms
)
NINF
(in
octant.ocean.state
)
NAN
(in
octant.ocean.step3d_t
)
nc_grid()
(in
octant.roms
)
NINF
(in
octant.ocean.step3d_t
)
NaN
(in
octant.ocean.step3d_t
)
NC_INT
(in
octant.extern.pupynere
)
NINF
(in
octant.ocean.time
)
nan
(in
octant.ocean.step3d_t
)
nc_N2
(in
octant.roms
)
NINF
(in
octant.ocean.velocity
)
NAN
(in
octant.ocean.time
)
nc_pstrain()
(in
octant.roms
)
node
(in
KDTree
)
NaN
(in
octant.ocean.time
)
NC_SHORT
(in
octant.extern.pupynere
)
not_equal
(in
octant.ocean.depths
)
nan
(in
octant.ocean.time
)
NC_VARIABLE
(in
octant.extern.pupynere
)
not_equal
(in
octant.ocean
)
NAN
(in
octant.ocean.velocity
)
nc_velocity
(in
octant.ocean.velocity
)
not_equal
(in
octant.ocean.state
)
NaN
(in
octant.ocean.velocity
)
nearest()
(in
time
)
not_equal
(in
octant.ocean.step3d_t
)
nan
(in
octant.ocean.velocity
)
nearest()
(in
time
)
not_equal
(in
octant.ocean.time
)
nanmask()
(in
octant.tools
)
nearest_date()
(in
time
)
not_equal
(in
octant.ocean.velocity
)
nbdy
(in
Bdy2d
)
nearest_index()
(in
time
)
NZERO
(in
octant.ocean
)
nbdy
(in
Bdy3d
)
negative
(in
octant.ocean.depths
)
NZERO
(in
octant.ocean.depths
)
nbytes
(in
octant.ocean.depths
)
negative
(in
octant.ocean
)
NZERO
(in
octant.ocean.state
)
nbytes
(in
octant.ocean
)
negative
(in
octant.ocean.state
)
NZERO
(in
octant.ocean.step3d_t
)
nbytes
(in
octant.ocean.state
)
negative
(in
octant.ocean.step3d_t
)
NZERO
(in
octant.ocean.time
)
nbytes
(in
octant.ocean.step3d_t
)
negative
(in
octant.ocean.time
)
NZERO
(in
octant.ocean.velocity
)
nbytes
(in
octant.ocean.time
)
negative
(in
octant.ocean.velocity
)
nbytes
(in
octant.ocean.velocity
)
NetCDFFile
(in
octant.extern.pupynere
)
O
o2_sat()
(in
octant.ocean.state
)
ogrid
(in
octant.ocean.state
)
ones_like
(in
octant.ocean.state
)
o2_saturation()
(in
octant.seawater
)
ogrid
(in
octant.ocean.step3d_t
)
ones_like
(in
octant.ocean.step3d_t
)
ocean
(in
octant
)
ogrid
(in
octant.ocean.time
)
ones_like
(in
octant.ocean.time
)
octant
ogrid
(in
octant.ocean.velocity
)
ones_like
(in
octant.ocean.velocity
)
ogrid
(in
octant.ocean.depths
)
ones_like
(in
octant.ocean.depths
)
OutOfRangeError
(in
octant.sw
)
ogrid
(in
octant.ocean
)
ones_like
(in
octant.ocean
)
P
pi
(in
octant.ocean.depths
)
PINF
(in
octant.ocean.time
)
psu2reference_salinity()
(in
octant.seawater
)
pi
(in
octant.ocean
)
PINF
(in
octant.ocean.velocity
)
pupynere
(in
octant.extern
)
pi
(in
octant.ocean.state
)
plotting
(in
octant
)
putdata()
(in
Bdy2d
)
pi
(in
octant.ocean.step3d_t
)
points()
(in
GreatCircle
)
putdata()
(in
Bdy3d
)
pi
(in
octant.ocean.time
)
power
(in
octant.ocean.depths
)
PZERO
(in
octant.ocean
)
pi
(in
octant.ocean.velocity
)
power
(in
octant.ocean
)
PZERO
(in
octant.ocean.depths
)
PINF
(in
octant.ocean
)
power
(in
octant.ocean.state
)
PZERO
(in
octant.ocean.state
)
PINF
(in
octant.ocean.depths
)
power
(in
octant.ocean.step3d_t
)
PZERO
(in
octant.ocean.step3d_t
)
PINF
(in
octant.ocean.state
)
power
(in
octant.ocean.time
)
PZERO
(in
octant.ocean.time
)
PINF
(in
octant.ocean.step3d_t
)
power
(in
octant.ocean.velocity
)
PZERO
(in
octant.ocean.velocity
)
Q
query()
(in
KDTree
)
query_ball_point()
(in
KDTree
)
query_ball_tree()
(in
KDTree
)
R
r_
(in
octant.ocean.depths
)
RAISE
(in
octant.ocean.depths
)
remove_grid()
(in
BoundaryInteractor
)
r_
(in
octant.ocean
)
RAISE
(in
octant.ocean.state
)
rho_stp()
(in
octant.ocean.state
)
r_
(in
octant.ocean.state
)
RAISE
(in
octant.ocean.step3d_t
)
rho_to_vert()
(in
octant.grid
)
r_
(in
octant.ocean.step3d_t
)
RAISE
(in
octant.ocean.time
)
right_shift
(in
octant.ocean.depths
)
r_
(in
octant.ocean.time
)
RAISE
(in
octant.ocean.velocity
)
right_shift
(in
octant.ocean
)
r_
(in
octant.ocean.velocity
)
read()
(in
NetCDFFile
)
right_shift
(in
octant.ocean.state
)
rad2deg
(in
octant.ocean.depths
)
reciprocal
(in
octant.ocean.depths
)
right_shift
(in
octant.ocean.step3d_t
)
rad2deg
(in
octant.ocean
)
reciprocal
(in
octant.ocean
)
right_shift
(in
octant.ocean.time
)
rad2deg
(in
octant.ocean.state
)
reciprocal
(in
octant.ocean.state
)
right_shift
(in
octant.ocean.velocity
)
rad2deg
(in
octant.ocean.step3d_t
)
reciprocal
(in
octant.ocean.step3d_t
)
rint
(in
octant.ocean.depths
)
rad2deg
(in
octant.ocean.time
)
reciprocal
(in
octant.ocean.time
)
rint
(in
octant.ocean
)
rad2deg
(in
octant.ocean.velocity
)
reciprocal
(in
octant.ocean.velocity
)
rint
(in
octant.ocean.state
)
radians
(in
octant.ocean.depths
)
Rectangle
(in
octant.extern.kdtree
)
rint
(in
octant.ocean.step3d_t
)
radians
(in
octant.ocean
)
remainder
(in
octant.ocean.depths
)
rint
(in
octant.ocean.time
)
radians
(in
octant.ocean.state
)
remainder
(in
octant.ocean
)
rint
(in
octant.ocean.velocity
)
radians
(in
octant.ocean.step3d_t
)
remainder
(in
octant.ocean.state
)
roms
(in
octant
)
radians
(in
octant.ocean.time
)
remainder
(in
octant.ocean.step3d_t
)
rot2d()
(in
octant.tools
)
radians
(in
octant.ocean.velocity
)
remainder
(in
octant.ocean.time
)
RAISE
(in
octant.ocean
)
remainder
(in
octant.ocean.velocity
)
S
s_
(in
octant.ocean.depths
)
SHIFT_DIVIDEBYZERO
(in
octant.ocean
)
sin
(in
octant.ocean.state
)
s_
(in
octant.ocean
)
SHIFT_DIVIDEBYZERO
(in
octant.ocean.depths
)
sin
(in
octant.ocean.step3d_t
)
s_
(in
octant.ocean.state
)
SHIFT_DIVIDEBYZERO
(in
octant.ocean.state
)
sin
(in
octant.ocean.time
)
s_
(in
octant.ocean.step3d_t
)
SHIFT_DIVIDEBYZERO
(in
octant.ocean.step3d_t
)
sin
(in
octant.ocean.velocity
)
s_
(in
octant.ocean.time
)
SHIFT_DIVIDEBYZERO
(in
octant.ocean.time
)
sinh
(in
octant.ocean.depths
)
s_
(in
octant.ocean.velocity
)
SHIFT_DIVIDEBYZERO
(in
octant.ocean.velocity
)
sinh
(in
octant.ocean
)
s_coordinate
(in
octant.depths
)
SHIFT_INVALID
(in
octant.ocean
)
sinh
(in
octant.ocean.state
)
salinity
(in
octant.sw
)
SHIFT_INVALID
(in
octant.ocean.depths
)
sinh
(in
octant.ocean.step3d_t
)
salt()
(in
octant.sw.salinity
)
SHIFT_INVALID
(in
octant.ocean.state
)
sinh
(in
octant.ocean.time
)
save_bry()
(in
BoundaryInteractor
)
SHIFT_INVALID
(in
octant.ocean.step3d_t
)
sinh
(in
octant.ocean.velocity
)
save_grid()
(in
BoundaryInteractor
)
SHIFT_INVALID
(in
octant.ocean.time
)
slice
(in
octant
)
sc
(in
s_coordinate
)
SHIFT_INVALID
(in
octant.ocean.velocity
)
soundvel()
(in
octant.sw.misc
)
sc
(in
Depths
)
SHIFT_OVERFLOW
(in
octant.ocean
)
split()
(in
Rectangle
)
ScalarType
(in
octant.ocean
)
SHIFT_OVERFLOW
(in
octant.ocean.depths
)
sqrt
(in
octant.ocean.depths
)
ScalarType
(in
octant.ocean.depths
)
SHIFT_OVERFLOW
(in
octant.ocean.state
)
sqrt
(in
octant.ocean
)
ScalarType
(in
octant.ocean.state
)
SHIFT_OVERFLOW
(in
octant.ocean.step3d_t
)
sqrt
(in
octant.ocean.state
)
ScalarType
(in
octant.ocean.step3d_t
)
SHIFT_OVERFLOW
(in
octant.ocean.time
)
sqrt
(in
octant.ocean.step3d_t
)
ScalarType
(in
octant.ocean.time
)
SHIFT_OVERFLOW
(in
octant.ocean.velocity
)
sqrt
(in
octant.ocean.time
)
ScalarType
(in
octant.ocean.velocity
)
SHIFT_UNDERFLOW
(in
octant.ocean
)
sqrt
(in
octant.ocean.velocity
)
sctypeDict
(in
octant.ocean.depths
)
SHIFT_UNDERFLOW
(in
octant.ocean.depths
)
square
(in
octant.ocean.depths
)
sctypeDict
(in
octant.ocean
)
SHIFT_UNDERFLOW
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The requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying.
7. Additional Terms.
“Additional permissions” are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:
- a) Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or
- b) Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or
- c) Prohibiting misrepresentation of the origin of that material, or requiring that modified versions of such material be marked in reasonable ways as different from the original version; or
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- f) Requiring indemnification of licensors and authors of that material by anyone who conveys the material (or modified versions of it) with contractual assumptions of liability to the recipient, for any liability that these contractual assumptions directly impose on those licensors and authors.
All other non-permissive additional terms are considered “further restrictions” within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).
However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.
Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.
An “entity transaction” is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party's predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.
11. Patents.
A “contributor” is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's “contributor version”.
A contributor's “essential patent claims” are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control” includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version.
In the following three paragraphs, a “patent license” is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant” such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.
If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying” means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.
A patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.
Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.
If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program.
Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an “about box”.
You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.
Note: the contents of this page were automatically generated from sources. Any errors or omissions are inadvertant and will be corrected on request by the rights-holders.


{% comment %} This is the search box and categories that appears on your course catalog pages. {% endcomment %}


nips nips2001 nips2001-132 nips2001-132-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Hilbert J. Kappen, Wim Wiegerinck
Abstract: The Cluster Variation method is a class of approximation methods containing the Bethe and Kikuchi approximations as special cases. We derive two novel iteration schemes for the Cluster Variation Method. One is a fixed point iteration scheme which gives a significant improvement over loopy BP, mean field and TAP methods on directed graphical models. The other is a gradient based method, that is guaranteed to converge and is shown to give useful results on random graphs with mild frustration. We conclude that the methods are of significant practical value for large inference problems. 1


WITNESS LIST
County Affairs Committee
July 21, 2016 - 12:30 PM
1115 Transformation Waiver
On:
Brieden, John (Washington County)
Deramus, Kevin (Washington County EMS)
Fuchs, Joy (Washington County)
Spurlin, Shayna (Texas A&M Health Science Center - 1115 Waiver Teams)
Child Protective Services
On:
Blackstone, Kristene (TX Dept. of Family and Protective Services)
Brieden, John (Washington County)
Mueller, Renee (Washington County Attorney Office)
Constable fee schedule and cost recovery
On:
Brieden, John (Washington County)
Gaas, Dennis (Self; JPCA)
Reeves, Jeff (Jeff Reeves elected official of Brazos County Pct. 1)
Registering, but not testifying:
On:
Butler, Isaac (JPCA)
Carter, Hezeklah (JPCA)
Gutierrez, Bobby (Self; Justice of the Peace and Constables Association of Texas)
Holle, Ken (Constable)
Lopez, Carlos (Self; Justices of the Peace and Constables Association of Texas)
Pantel, Jama (Justice of the Peace and Constables Association)
Shepherd, William (Washington County Constable Pct. 2)
County Cybersecurity
On:
Brieden, John (Washington County)
Van Dresar, Robert (Self; Burleson County)
Registering, but not testifying:
On:
Beavers, Doug (Burleson Co.)
Public dissemination of mugshots and criminal records
On:
Hanak, Otto (Washington County Sheriff's Office)
Harmon, Micah (Self; Sheriff's Association of Texas)
Sowell, Donald (Sheriff's Association of Texas)
1
WITNESS LIST
Texas Commission on Jail Standards
On:
Green, Jerry (Washington County S.O. - Jail)
Hanak, Otto (Washington County Sheriffs Office)
Harmon, Micah (Self; Sheriff's Association of Texas)
Sowell, Donald (Self; Sheriff's Association of Texas)
Wood, Brandon (Commission on Jail Standards)
2


Snakeflake is a discrete nonserial ID generator like Snowflake and Sonyflake, implemented in Python.
Insert icon here when I make a better one.
Check out the GitHub for an example usage.
Contents:
Index
Module Index
Search Page
A versatile ID generation system