|
Polar,
Environment,
and Science (POLES, nationwide). The POLES survey involved random-sample telephone interviews (cell and landline) with respondents from all US states, carried out in two stages: before the 2016 presidential elections (August,
n = 704) and immediately afterwards (November/December,
n = 707). Response rates in four subsamples of the POLES survey ranged from 15 to 30%, calculated by AAPOR definition 3.[
32] Several papers have focused on POLES results.[
33][
34]
|
|
Granite State Poll (GSP, New Hampshire statewide). These landline and cell telephone surveys interview independent, statewide random samples of New Hampshire residents four times each year. Along with standard background and political questions, the GSP often carries items about environment or science. New Hampshire responses on environmental questions commonly fall close to national benchmarks. Several recent papers make comparisons between New Hampshire and nationwide surveys.[
14][
35] The GSP from April 2010 to October 2018 conducted 20,786 interviews that included our climate-change question, and from July 2012 to October 2018 conducted 7,707 with the question about renewable energy. Median response rate over this period was 21.5 percent.
|
|
Communities and Forests in Oregon (CAFOR, northeast Oregon). Under the CAFOR project, landline and cell telephone surveys involving independent random samples of northeast Oregon residents were conducted in four stages: September/October 2011 (
n = 1,585 from Baker, Union and Wallowa Counties); August/October 2014 (
n = 1,752, from the same three counties along with Crook, Grant, Umatilla and Wheeler Counties); October/November 2015 (
n = 651, repeating the seven counties from 2014); and September 2018 (
n = 1,097) in just the three original counties. Median response rate of the CAFOR surveys was 38 percent. For consistency, our analysis in this paper focuses on 3,782 interviews from only those counties (Baker, Union and Wallowa) that were surveyed in all four years. Voting patterns in all of these northeast Oregon counties tend to be politically conservative, and surveys find lower-than-national recognition of anthropogenic climate change.[
14][
23]
|
|
North Country (northern New England). In summer 2017, researchers with the Carsey School of Public Policy (University of New Hampshire) conducted this random-sample cell and landline telephone survey of 1,650 residents in four contiguous northern New England counties, collectively termed the North Country: Coös and Grafton Counties, New Hampshire; Essex County, Vermont; and Oxford County, Maine. Designed to assess changes in residents’ perceptions of their rural communities, the 2017 survey (response rate of 19%) replicated some questions from earlier surveys, but also included new environmental and climate items. Some results are analyzed in papers by Hamilton et al.[
24][
25]
|


Moodie
5 movie recommendations in 2 easy steps!
Upload a Selfie!
This picture interpreter will use your selfie to help determine what movies you're in the mood for!
Mood Quiz
Please answer the questions below truthfully. Your answers will help determine the movies best matching your current mood!
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Please rate your current physical comfort. Rate from uncomfortable (left) to very comfortable (right).
How awake are you? Rate from very tired (left) to very awake (right).
Please rate your current employment from banal (left) to inspiring (right)
How many roads must a man walk down before you can call him a man?
How often do you read for fun? Seldom (left) or often (right)
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{% extends "layouts/theme.html" %} {% block content %}
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People's Open is a community-owned and -operated wireless network in Oakland, California
Community driven, open source, peer-to-peer internet


| Package | Description |
|---|---|
| com.valentine.esp | |
| com.valentine.esp.constants | |
| com.valentine.esp.packets | |
| com.valentine.esp.packets.request | |
| com.valentine.esp.packets.response | |
| com.valentine.esp.statemachines |
| Modifier and Type | Field and Description |
|---|---|
Devices |
ValentineClient.m_valentineType |
| Modifier and Type | Method and Description |
|---|---|
static Devices |
PacketQueue.getV1Type()
Get the current V1 type.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ValentineClient.doFactoryDefault(Devices _device)
Sends a RequestFactoryDefault packet to the indicated device.
|
void |
ValentineClient.getSerialNumber(java.lang.Object _callbackObject,
java.lang.String _function,
Devices _destination)
Gets the serial number of the requested device.
|
void |
ValentineClient.getVersion(java.lang.Object _callbackObject,
java.lang.String _function,
Devices _destination)
Gets the version of the requested device.
|
static void |
PacketQueue.initOutputQueue(Devices v1Type,
boolean clearOutputQueue,
boolean holdoffOutput)
Initialize the output queue.
|
static void |
PacketQueue.setNewV1Type(Devices v1Type)
Tells the output queue to change the V1 type.
|
| Modifier and Type | Method and Description |
|---|---|
static Devices |
Devices.fromByteValue(byte val)
Returns a
Device enum that corresponds to val. |
static Devices |
DevicesLookup.getConstant(byte _value)
Converts a byte value to a Devices value
|
static Devices |
Devices.valueOf(java.lang.String name)
Returns the enum constant of this type with the specified name.
|
static Devices[] |
Devices.values()
Returns an array containing the constants of this enum type, in
the order they are declared.
|
| Modifier and Type | Method and Description |
|---|---|
Devices |
ESPPacket.getDestination()
Retrieve the
Device enum for this packet's destination identifier. |
Devices |
ESPPacket.getOrigin()
Retrieve the
Device enum for this packet's originator identifier. |
Devices |
ESPPacket.getV1Type()
Retrieve the
Device enum for this packet's valentine type. |
| Modifier and Type | Method and Description |
|---|---|
static ESPPacket |
ESPPacket.makeFromBuffer(java.util.ArrayList<java.lang.Byte> buffer,
ConnectionType type,
Devices lastV1Type)
Takes an ArrayList of bytes and builds the resulting packet from that array.
|
void |
ESPPacket.setNewV1Type(Devices newV1Type)
Changes the Valentine One type used to build this packet.
|
| Constructor and Description |
|---|
InfDisplayData(Devices _destination) |
InfV1Busy(Devices _destination) |
UnknownPacket(Devices _destination) |
| Constructor and Description |
|---|
ResponseAlertData(Devices _destination) |
ResponseBatteryVoltage(Devices _destination) |
ResponseDataError(Devices _destination) |
ResponseDataReceived(Devices _destination) |
ResponseDefaultSweepDefinitions(Devices _destination) |
ResponseMaxSweepIndex(Devices _destination) |
ResponseRequestNotProcessed(Devices _destination) |
ResponseSavvyStatus(Devices _destination) |
ResponseSerialNumber(Devices _destination) |
ResponseSweepDefinitions(Devices _destination) |
ResponseSweepSections(Devices _destination) |
ResponseSweepWriteResult(Devices _destination) |
ResponseUnsupported(Devices _destination) |
ResponseUserBytes(Devices _destination) |
ResponseVehicleSpeed(Devices _destination) |
ResponseVersion(Devices _destination) |
| Constructor and Description |
|---|
GetAllSweeps(boolean _clearCacheBeforeRead,
Devices _valentineType,
ValentineESP _valentineESP,
java.lang.Object _callbackObject,
java.lang.String _callbackFunction,
java.lang.Object _errCallbackObject,
java.lang.String _errCallbackFunction)
Constructor for the GetAllSweeps state machine.
|
WriteCustomSweeps(java.util.ArrayList<SweepDefinition> _userDefintions,
Devices _valentineType,
ValentineESP _valentineESP,
java.lang.Object _finalCallbackObject,
java.lang.String _finalCallbackFunction,
java.lang.Object _errorCallbackObject,
java.lang.String _errorCallbackFunction)
Initializes the WriteCustomSweeps state machine with the custom sweeps,
ValentineESP object, and the success/failure callbacks.
|


Salmon Cookies Are The Treat So Sweet, You'll Swim Upstream To Get Them!
It's a beautiful, sunny morning in high summer in the North Cascades. You're making your way down to the
stony bank of a clear, cool river, and you see a beautiful sight: slick, shining salmon leaping out of the
rapids as they make their way up. Suddenly, you're hungry . . . BECAUSE YOU'RE A BEAR!
. . . Or are you?
 Or maybe you're just any animal that walks on two legs. Because if there's one
thing we humans in the Pacific Northwest Know, it's that nothing has the same beautiful taste of
salmon caught right out of the water. Healthy, hearty, a little sweet, and savory for every bite, salmon was
made for eating every way from
sushi to sous vide. It's too bad salmon just isn't the kind of thing you can easily take with you as a snack
on the go.
. . . Until now.
Here at Salmon Cookies, we've come up with yet another preparation in which this perfect pesce (that's
Italian for
'fish') can be eaten—and you can probably guess what's coming . . . Yes, we're talking about salmon cookies!
Skeptical? Of course not, you came to a website called Salmon Cookies for a reason. But in case you are, ask
yourself this: what could be better than bringing together mother nature's most delicious fish in the sea,
with the pinnacle of human civilization's achievements in baking? Nothing. Want to know why? Because like any
couple that was always meant to be together, they bring out the best in each other. The freshness of salmon,
and the chewy goodness of a cookie, all in handy snacking form.
If youre mouth is watering, then you're in luck: Salmon Cookies are available at our five international
locations right now—and if you're not near one, don't worry, because we can ship them to you! Whatever your
needs, we're going to do all we can to help you get your hands—or claws—on one of our cookies . . . Or maybe a
dozen .
. . Or maybe a whole school! We're sure you've going to love them, human OR bear.


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--- layout: page title: About header-img: "img/about-bg.png" ---
I am a digital transformation specialist whose primary expertise is in Content Management.
I have worked across government agencies redefining the way they deliver services to its users. I am passionate about innovation, disruptive technologies and my new love, data science. Give me a call if you are interested in my services or if you just want to exchange ideas on analytics, sports and web development and not necessarily in that order :)


| tbl_uvregression {gtsummary} | R Documentation |
This function estimates univariate regression models and returns them in a publication-ready table. It can create univariate regression models holding either a covariate or outcome constant.
For models holding outcome constant, the function takes as arguments a data frame,
the type of regression model, and the outcome variable y=
. Each column in the
data frame is regressed on the specified outcome. The tbl_uvregression
function arguments are similar to the tbl_regression arguments. Review the
tbl_uvregression vignette
for detailed examples.
You may alternatively hold a single covariate constant. For this, pass a data
frame, the type of regression model, and a single
covariate in the x=
argument. Each column of the data frame will serve as
the outcome in a univariate regression model. Take care using the x
argument
that each of the columns in the data frame are appropriate for the same type
of model, e.g. they are all continuous variables appropriate for lm, or
dichotomous variables appropriate for logistic regression with glm.
tbl_uvregression( data, method, y = NULL, x = NULL, method.args = NULL, exponentiate = FALSE, label = NULL, include = everything(), tidy_fun = NULL, hide_n = FALSE, show_single_row = NULL, conf.level = NULL, estimate_fun = NULL, pvalue_fun = NULL, formula = "{y} ~ {x}", add_estimate_to_reference_rows = NULL, show_yesno = NULL, exclude = NULL )
data |
Data frame to be used in univariate regression modeling. Data frame includes the outcome variable(s) and the independent variables. Survey design objects are also accepted. |
method |
Regression method (e.g. lm, glm, survival::coxph,
|
y |
Model outcome (e.g. |
x |
Model covariate (e.g. |
method.args |
List of additional arguments passed on to the regression
function defined by |
exponentiate |
Logical indicating whether to exponentiate the
coefficient estimates. Default is |
label |
List of formulas specifying variables labels,
e.g. |
include |
Variables to include in output. Input may be a vector of
quoted variable names, unquoted variable names, or tidyselect select helper
functions. Default is |
tidy_fun |
Option to specify a particular tidier function if the
model. Default is to use |
hide_n |
Hide N column. Default is |
show_single_row |
By default categorical variables are printed on multiple rows. If a variable is dichotomous (e.g. Yes/No) and you wish to print the regression coefficient on a single row, include the variable name(s) here–quoted and unquoted variable name accepted. |
conf.level |
Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval. |
estimate_fun |
Function to round and format coefficient estimates. Default is style_sigfig when the coefficients are not transformed, and style_ratio when the coefficients have been exponentiated. |
pvalue_fun |
Function to round and format p-values.
Default is style_pvalue.
The function must have a numeric vector input (the numeric, exact p-value),
and return a string that is the rounded/formatted p-value (e.g.
|
formula |
String of the model formula.
Uses glue::glue syntax. Default is |
add_estimate_to_reference_rows |
add a reference value. Default is FALSE |
show_yesno |
DEPRECATED |
exclude |
DEPRECATED |
A tbl_uvregression
object
Example 1
Example 2
The default method for tbl_regression()
model summary uses broom::tidy(x)
to perform the initial tidying of the model object. There are, however,
a few models that use modifications.
"survreg"
: The scale parameter is removed, broom::tidy(x) %>% dplyr::filter(term != "Log(scale)")
"multinom"
: This multinomial outcome is complex, with one line per covariate per outcome (less the reference group)
"gam"
: Uses the internal tidier tidy_gam()
to print both parametric and smooth terms.
"lmerMod"
, "glmerMod"
, "glmmTMB"
, "glmmadmb"
, "stanreg"
, "brmsfit"
: These mixed effects
models use broom.mixed::tidy(x, effects = "fixed")
. Specify tidy_fun = broom.mixed::tidy
to print the random components.
The N reported in the output is the number of observations
in the data frame model.frame(x)
. Depending on the model input, this N
may represent different quantities. In most cases, it is the number of people or
units in your model. Here are some common exceptions.
Survival regression models including time dependent covariates.
Random- or mixed-effects regression models with clustered data.
GEE regression models with clustered data.
This list is not exhaustive, and care should be taken for each number reported.
Daniel D. Sjoberg
See tbl_regression vignette for detailed examples
Other tbl_uvregression tools:
add_global_p()
,
add_q()
,
bold_italicize_labels_levels
,
inline_text.tbl_uvregression()
,
modify
,
tbl_merge()
,
tbl_stack()
# Example 1 ---------------------------------- tbl_uv_ex1 <- tbl_uvregression( trial[c("response", "age", "grade")], method = glm, y = response, method.args = list(family = binomial), exponentiate = TRUE ) # Example 2 ---------------------------------- # rounding pvalues to 2 decimal places library(survival) tbl_uv_ex2 <- tbl_uvregression( trial[c("ttdeath", "death", "age", "grade", "response")], method = coxph, y = Surv(ttdeath, death), exponentiate = TRUE, pvalue_fun = function(x) style_pvalue(x, digits = 2) )


General Information
This tab displays general information retrieved from the
CPUID instruction, such as:
- The processor manufacturer, identifying this as a genuine
Intel® processor by returning the non-zero terminated ASCII string 'GenuineIntel'.
- The processor brand string, which is a zero terminated
ASCII brand identifier string for the system's processor.
- The family, model, stepping, and extended family and
model number which identify the type of processor this is. The
extended family number only needs to be checked when the family number if 15
and the extended model number only needs to be checked when the family
number is either 6 or 15.
- The time stamp counter frequency, and an indication as to whether this frequency can vary or not.


Documents you open are automatically stored in Download/Adobe Reader folder on your device.
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An algorithm for computing a distance metric
which is an approximation to the Hausdorff Distance
based on a discretization of the input
Geometry
.
The algorithm computes the Hausdorff distance restricted to discrete points
for one of the geometries.
The points can be either the vertices of the geometries (the default),
or the geometries with line segments densified by a given fraction.
Also determines two points of the Geometries which are separated by the computed distance.
This algorithm is an approximation to the standard Hausdorff distance.
Specifically,
for all geometries a, b: DHD(a, b) <= HD(a, b)
The approximation can be made as close as needed by densifying the input geometries.
In the limit, this value will approach the true Hausdorff distance:
DHD(A, B, densifyFactor) -> HD(A, B) as densifyFactor -> 0.0
The default approximation is exact or close enough for a large subset of useful cases.
Examples of these are:
- computing distance between Linestrings that are roughly parallel to each other,
and roughly equal in length. This occurs in matching linear networks.
- Testing similarity of geometries.
An example where the default approximation is not close is:
A = LINESTRING (0 0, 100 0, 10 100, 10 100)
B = LINESTRING (0 100, 0 10, 80 10)
DHD(A, B) = 22.360679774997898
HD(A, B) ~= 47.8


Train Schedule
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Time Away


 # Advances in Forward Sufficient Dimension Reduction for Statistical Learning
 ## Harris Quach (joint work with Dr. Bing Li)
 Date: 2021-07-19 (updated: 2021-07-17) --- class: left, middle # Acknowledgements I would like to thank my committee members for their time and support: - Bing Li - Bharath K. Sriperumbudur - Yanyuan Ma - Alexei Novikov --- class: inverse, middle  # .bg-text.center[Overview] .md-text[ 1. Forward Sufficient Dimension Reduction for Categorical and Ordinal Response 2. Forward Sufficient Dimension Reduction for Categorical and Ordinal Response with functional predictor 3. Forward Sufficient Dimension Reduction for functional response with functional predictor ] --- count: false class: left, top # Why Sufficient Dimension Reduction?
 "...I’ve never been comfortable with the way in which our discipline has embraced sparsity to the exclusion of everything else. I mentioned before that I like the availability of options in statistics, but for a long time now the prevailing attitude toward high-dimensional problems has been that its natural to assume sparsity. I find no sense in which it’s natural. That overwhelming emphasis on sparsity has, I think, kept us from seeing other solutions and options. To be clear, I have nothing against sparsity per se, and I think it’s a reasonable modeling construct. But it’s not reasonable just because you have high-dimensional data." .right[~ *Dennis Cook* (2021)] .right[(Bura, Li, Li, Nachtsheim, Pena, Setodji, and Weiss, 2021)]
 --- class: left, top # What is Sufficient Dimension Reduction? Suppose we have a large dataset with some response `\(Y \in \R^m\)` and predictors `\(X \in \R^p\)`. - When `\(p\)` is large, lower dimensional summaries of `\(X\)` are helpful for visualization and application of conventional statistical methods - Finding a lower dimensional summary of `\(X\)` means finding `\(\beta \in \R^{p \times d}\)`, where `\(d < p\)`, in order to construct the lower dimensional summary `\(\beta^\top X\)` - Sufficient Dimension Reduction (SDR) are approaches for finding `\(\beta\)` such that `\(\beta^\top X\)` retains all relevant information about `\(Y\)` - Inverse and Forward SDR describe general SDR approaches for finding such a dimension reduction `\(\beta\)` - `\(\beta\)` can preserve different information by satisfying different criteria. --- class: left, top # Central Subspaces The information preserved by `\(\beta\)` is characterized by conditional independence: - `\(Y \indep X | \beta^\top X\)`; (Li, 2018) - `\(\beta\)` preserves all information between `\(Y\)` and `\(X\)` and `\(\mathrm{span}(\beta)\)` is an SDR subspace for `\(Y|X\)`; the smallest such subspace is called the **Central Subspace**. - `\(Y \indep E(Y|X) | \beta^\top X\)`; (Cook and Li, 2002; Ma and Zhu, 2014) - `\(\beta\)` preserves all information between `\(Y\)` and `\(E(Y|X)\)` and `\(\mathrm{span}(\beta)\)` is an SDR subspace for `\(E(Y|X)\)`; the smallest such subspace is called the **Central Mean Subspace**. -- **NOTE** We actually want the subspace generated by `\(\beta\)`, i.e. `\(\mathrm{span}(\beta)\)`. - **When we say we are estimating `\(\beta\)`, we mean estimating a basis for the subspace generated by `\(\beta\)`, i.e a basis for `\(\mathrm{span}(\beta)\)`.** --- class: left, top # Alternative Subspaces The information preserved by `\(\beta\)` is characterized by conditional independence: - `\(Y - E(Y|X) \indep \mathrm{Var}(Y|X) | \beta^\top X\)`; (Zhu and Zhu, 2009) - `\(\beta\)` preserves all information between `\(Y - E(Y|X)\)` and `\(\mathrm{Var}(Y|X)\)`;  - `\(Y \indep Q_\tau(X) | \beta^\top X\)`; (Kong and Xia, 2012; Luo, Li, and Yin, 2014) - `\(\beta\)` preserves all information between `\(Y\)` and `\(Q_\tau(X)=\inf\{y : P(Y \leq y |X) \geq \tau\}\)`;  - `\(Y \indep \theta(X) | \beta^\top X\)`; (Luo, Li, and Yin, 2014) - `\(\beta\)` preserves all information between `\(Y\)` and `\(\theta(X)\)`, where `\(\theta(X)\)` is defined through a conditional statistical functional; - `\(N(B_1),\ldots, N(B_k) \indep X(B_1),\ldots,X(B_k) | \beta^\top X(B_1),\ldots,\beta^\top X(B_k)\)`; (Guan and Wang, 2010) - `\(\beta\)` preserves all information between the counting process `\(N\)` and the Gaussian random field `\(X\)` over sets `\(B_1,\ldots,B_k\)`; --- class: left, top # Recent Advances in SDR - Semi-parametric efficient SDR (Ma and Zhu, 2012; Ma and Zhu, 2013; Ma and Zhu, 2014) - gradient-based SDR using RKHS (Fukumizu and Leng, 2014) - Ladle and Predictor Augmentation Estimator for `\(d\)` (Luo and Li, 2020; Luo and Li, 2016) - Nonlinear SDR (Lee, Li, and Chiaromonte, 2013) - Nonlinear and Linear Functional SDR (Li and Song, 2017; Li and Song, 2021) - Post-dimension Reduction Inference (Kim, Li, Yu, and Li, 2020) --- class: inverse, center, middle # .bg-text[Inverse and Forward Linear SDR] --- class: left, top # Motivating Example: Consider a response `\(Y\)` and predictor `\(X = (X_1, X_2) \in [0,1]^2\)`. Let `\(Y=X_1^2\)`. Then `\(Y= (\beta^\top X)^2\)`, where `\(\beta = (1,0) \in \R^2\)`. - we want to recover `\(span(\beta) = \{ (c,0): c \in \R\}\)`; we take `\(d=1\)` as known. .center[  ] --- class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ]  --- count: false class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ] --- count: false class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ] --- count: false class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ] --- count: false class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ] -- - 'Inverse' because we estimate `\(E(X|Y)\)`. --- class: left, top   # Motivating Example: ## Inverse Regression for SDR - Drawbacks .center[  ] --- count: false class: left, top # Motivating Example: ## Inverse Regression for SDR - Drawbacks .center[  ] --- count: false class: left, top # Motivating Example: ## Inverse Regression for SDR - Drawbacks .center[  ] --- count: false class: left, top # Motivating Example: ## Inverse Regression for SDR - Drawbacks .center[  ] -- - Inverse methods require assumptions on the support of the predictor. --- class: left, top # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, and Zhu, 2002) .center[  ] --- count: false class: left, top   # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] --- count: false class: left, top   # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] --- count: false class: left, top   # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] -- - "Forward" Regression because we are estimating `\(E(Y|x)\)` and `\(\partial E(Y|x)/\partial x^\top\)` --- count: false class: left, top   # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] --- count: false class: left, top # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] --- class: left, top # Developments in Forward Regression for SDR ## Summary of Thesis: - Generalize these two pictures to categorical and ordinal response - Generalize these two pictures to functional data .center[   ] --- class: inverse, left, middle  # .center[.bg-text[Forward SDR for Categorical and Ordinal Response] ] 1. Multivariate Link Functions 2. Outer Product of Canonical Gradients 4. Minimum Average Deviance Estimation 3. K-means Tuning 4. Simulations and Applications --- class: left, top # Forward Regression for SDR .center[   ] - OPG applies to uni-variate, continuous `\(Y\)` - Doesn't work as well when `\(Y\)` is categorical or ordinal - OPG fits a local linear regression - **Proposal: Fit a local multivariate GLM** --- class: left, top # Why fit a local multivariate GLM? We are interested in categorical and ordinal `\(Y\)`. -- Categorical variables are linear exponential families (hence GLM) because they are a special case of the multinomial - the multivariate Logistic and Expit functions are the canonical and inverse canonical links -- **Contribution:** We show ordinal variables can be represented explicitly as a linear exponential family by computing its canonical link, inverse canonical link and cumulant generating function. - the canonical link the multivariate **Adjacent Categories** logistic link (Agresti, 2010) --- class: left, top # Ordinal Response Suppose `\(Y \in \{1,...,m\}\)` is an ordinal-categorical variable for `\(m\)` ordered categories. - We can represent `\(Y\)` with a vector `\(T = (T_1,...T_{m-1}) \in \{0,1\}^{m-1}\)`; - We can interpret the entries of `\(T\)` as `\(T_j = I\{Y > j\}\)` for categories `\(j=1,\ldots,m-1\)`  - Eg. `\(m=5\)`; if `\(Y=3\)`, then `\(T=(1,1,0,0)\)`; if `\(Y=1\)`, then `\(T=(0,0,0,0)\)` --- class: left, top # Ordinal Response We show the random vector `\(T\)` has log-likelihood: `\begin{align*} \ell(\theta; T) = \theta^{\top} T - \log ( 1 + e_{1}^{\top} P L \exp (L \theta ) ) , \end{align*}` `\(P\)` is a permutation matrix and `\(L\)` is lower triangular matrix of `\(1\)`'s. - So `\(T\)` is a linear exponential family (hence GLM). - We say the random vector `\(T\)` has a **ordinal-categorical (Or-Cat)** distribution. -- The **adjacent-categories (Ad-Cat)** link is the canonical link `\begin{align*} \theta(\tau) = \log \left\{ \mathrm{diag}[ (P^{-1} - I)\tau ]^{-1} (P^{-1} - I)\tau \right \}, \end{align*}` where `\(E(T)=\tau\)`. -- The inverse canonical link (i.e. mean function) is `\begin{align*} \tau(\theta) =\frac{Q P L \exp (L \theta ) }{1 + e_1^{\top} P L \exp (L \theta ) } , \end{align*}` where `\(Q\)` is a difference matrix --- class: left, top # Forward Regression for SDR .center[   ] - OPG developed for uni-variate, continuous `\(Y\)` - Doesn't work as well when `\(Y\)` is categorical or ordinal - OPG fits a local linear regression - **Proposal: Fit a local multivariate GLM** --- class: left, top # Forward Regression for SDR ##Proposal: Fit a local multivariate GLM Existing work in this direction: 1. Generalized Single Index Model (GSIM): Lambert-Lacroix and Peyre (2006) - Local Linear GLM for uni-variate Y$; - Goal was to generalize OPG, but method is actually the Average Derivative Estimator (ADE) Härdle and Stoker (1989); - ADE has known drawbacks; e.g. requires gradient has non-zero mean. 2. Minium Average Deviance Estimation (MADE): Adragni (2018) - Local Linear GLM for uni-variate `\(Y\)` - Generalizes the Minimum Average Variance Estimator (MAVE) of Xia, Tong, Li, et al. (2002) **Can't handle multi-labels simultaneously. Only binary `\(Y\)` can be reduced to uni-variate `\(Y\)`.

 Eg. Response with 3 labels needs bi-variate `\(Y\)` representation** --- class: inverse, center, middle  # .bg-text[Outer Product of Canonical Gradients (OPCG)] --- class: left, top   # Generalized Forward Regression for SDR .center[  ] - In OPG, we fit a linear regression about `\(x_0\)`, i.e. we minimize `\begin{align*} \{Y - a_{x_0} + B_{x_0}^\top(X-x_0) \}^\top \{Y - a_{x_0} + B_{x_0}^\top(X-x_0) \} \end{align*}` and estimate `\(\partial E(Y|X=x_0)/ \partial x^\top\)` using `\(\hat B_{x_0}\)`.  --- class: left, top # Generalized Forward Regression for SDR .center[  ] - Instead, fit a multivariate GLM about `\(x_0\)` and minimize `\begin{align*} -\ell (a_{x_0}, B_{x_0},x_0) = - [a_{x_0} + B_{x_0}^\top(x-x_0)]^\top y + b[a_{x_0} + B_{x_0}^\top(x-x_0)] . \end{align*}` We will use the minimizer `\(\hat B_{x_0}\)`. --- class: left, top # Why use `\(B_{x_0}\)` in the GLM? ## Our Dimension Reduction Assumption - In OPG, the dimension reduction assumption is `\(\beta\)` satisfies `\(Y \indep E(Y|X)|\beta^\top X\)`. - This is equivalent to `\(E(Y|X) = E(Y|\beta^\top X)\)`. - Our the dimension reduction assumption is `\(\beta\)` satisfies `\(Y \indep \theta(X)|\beta^\top X\)`; - `\(\theta(X)\)` is the canonical parameter of the multivariate GLM, through which `\(X\)` relates to `\(Y\)` - Our dimension reduction assumption is equivalent to `\(\theta(X) = \theta(\beta^\top X)\)` - For linear exponential families, `\(\theta(X) = link^{-1}(E(Y|X))\)`, so our dimension reduction assumption is the same as OPG. --- class: left, top # Why use `\(B_{x_0}\)` in the GLM? ## Our Dimension Reduction Assumption - Because `\(\theta(X) = \theta(\beta^\top X)\)`, the gradient of `\(\theta(x)\)` satisfies `\begin{align*} \partial \theta(x)^\top /\partial x = \beta \partial \theta(u)^\top/\partial u \end{align*}` - That is, `\(\mathrm{span}(\partial \theta(x)^\top /\partial x) \subseteq \mathrm{span}(\beta)\)` - We say `\(\partial \theta(x)^\top /\partial x\)` is *unbiased* for the central mean subspace. --- class: left, top # Why use `\(B_{x_0}\)` in the GLM? ## Our Dimension Reduction Assumption - In OPG, `\(\hat B_{x_0}\)` estimates the gradient of `\(E(Y|X=x)\)` at `\(x_0\)`, i.e. `\(\partial E(Y|X=x_0) /\partial x^\top\)`. - Similarly, the `\(\hat B_{x_0}\)` obtained from minimizing `\begin{align*} -\ell_0(a_0, B_0;y,x,x_0) = -[a_0 + B_0^\top(x-x_0)]^\top y + b[a_0 + B_0^\top(x-x_0)] \end{align*}` `\(\hat B_{x_0}\)` estimates the gradient of `\(\theta(x)\)` at `\(x_0\)`, i.e. `\(\partial \theta(x_0) /\partial x^\top\)`. ** `\(\hat B_{x_0}\)` estimates the canonical gradient at `\(x_0\)`.** --- class: left, top # Outer Product of Canonical Gradients (OPCG) Given a random sample `\(Y_{1:n}\)`, `\(X_{1:n}\)`, fit a local linear multivariate GLM about `\(x_0\)` by minimizing `\begin{align*} & -\ell (a_{x_0}, B_{x_0}, x_0, ;X_{1:n}, Y_{1:n}) \\ = & \frac 1n \sum_{i=1}^n K \bigg ( \frac{X_i - x_0}{h} \bigg ) \{-[a_{x_0} + B_{x_0}^\top (X_i - x_0)]^\top Y_i + b[a_{x_0} + B_{x_0}^\top (X_i - x_0) ] \} \end{align*}` where `\(b(\cdot)\)` determines the GLM, and `\(K(\cdot)\)` is a kernel weight with bandwidth `\(h\)`. The minimizer `\(\hat B_{x_0}\)` is used to estimate `\(\partial \theta(x_0) /\partial x^\top\)`. .center[ <img class="image" src="images/fsdr_plot3-1.png" width="35%"> ] --- class: left, top # Outer Product of Canonical Gradients (OPCG) We fit a local linear multivariate GLM about each `\(X_j\)`, for `\(j=1,...,n\)`, by minimizing the full negative local linear log-likelihood: `\begin{align*} & L(a_1,..,a_n, B_1,...,B_n; X_{1:n}, Y_{1:n}) \\ = & -\frac {1}{n} \sum_{j,i=1}^n K \bigg ( \frac{X_i - X_j}{h} \bigg ) \{[a_{j} + B_{j}^\top (X_i - X_j)]^\top Y_i - b(a_{j} + B_{j}^\top (X_i - X_j)) \} . \end{align*}` This provides a collection of minimizers `\(\hat B_1,\ldots,\hat B_j\)` that estimate `\(\partial \theta(X_j)/\partial x^\top\)`. .center[ <img class="image" src="images/fsdr_plot4.png" width="35%"> ] --- class: left, top # The OPCG Estimator We use `\(\hat B_1,\ldots, \hat B_n\)` to construct the average outer product `$$\hat \Lambda_{\mathrm{opcg}} = \frac 1n \sum_{j=1}^n \hat B_j \hat B_j^\top.$$` The **Outer Product of Canonical Gradients (OPCG) Estimator, `\(\hat \beta_{opcg}\)`**, is the leading `\(d\)` eigenvectors of `\(\hat \Lambda_{\mathrm{opcg}}\)` corresponding to the `\(d\)` largest eigenvalues. .center[  ] --- class: left, top # Properties related to OPCG ## Under some regularity assumptions...
 Let \begin{align*} \Lambda_{\mathrm{opcg}} = E \bigg \{ \frac{\partial \theta(X)^\top}{\partial x} \frac{\partial \theta(X)}{\partial x^\top} \bigg \} . \end{align*} Under some regularity assumptions, \(\mathrm{span}( \Lambda_{\mathrm{opcg}} ) = \mathrm{span}( \beta ) \).
 --

 Let \(\eta\) be the leading \(d\) eigenvectors of \(\Lambda_{\mathrm{opcg}}\). Then, under some regularity, compactness and bandwidth assumptions, we have \begin{align*} \| \hat \beta_{\mathrm{opcg}} - \eta \|_F = O_{a.s} ( h + h^{-1} \delta_{ph} + ( \log(n)/n )^{1/2} ), \end{align*} where \(\delta_{ph} = \sqrt{ \frac{\log n}{ nh^p} }\), \( h \downarrow 0\), and \( h^{-1}\delta_{ph} \to 0\).
 --- class: left, top # A more direct approach to `\(\beta\)` - Recall: the gradient is unbiased `\begin{align*} \partial \theta(x)^\top /\partial x = \beta \partial \theta(u)^\top/\partial u \end{align*}` -- We can incorporate this into the objective function directly by minimizing `\begin{align*} & L(\beta, a_1,..,a_n, B_1,...,B_n; X_{1:n}, Y_{1:n}) \\ = & -\frac {1}{n} \sum_{j,i=1}^n W_{ij}(h) \{[a_{j} + B_{j}^\top \beta^\top (X_i - X_j)]^\top Y_i - b(a_{j} + B_{j}^\top \beta^\top (X_i - X_j)) \} . \end{align*}` where `\(W_{ij}(h) = K \bigg ( \frac{X_i - X_j}{h} \bigg )\)`. - Alternate between minimizing w.r.t. `\(\beta\)` and `\(a_1,\ldots,a_n, B_1,\ldots,B_n\)` until `\(\beta\)` estimates converge. - Minimizer of `\(\beta\)` is the **Minimum Average Deviance Estimator (MADE), `\(\hat \beta_{\mathrm{made}}\)`**. --- class: left, top # MADE - MADE generalizes the MAVE estimator by (Xia, Tong, Li, et al., 2002) - Our MADE estimator, `\(\hat \beta_{\mathrm{made}}\)`, is the multivariate version proposed by Adragni (2018) - But our MADE can handle multi-label problems simulatenously using the multivariate link function. --- class: left, top # Refined OPCG and Refined MADE We can refined our estimators `\(\hat \beta_{\mathrm{opcg}}\)` and `\(\hat \beta_{\mathrm{made}}\)` through an iterative procedure. For Refined OPCG: 1. Estimate `\(\hat \beta_{\mathrm{opcg}}\)`. 2. Replaced the kernel weight `\(K (h^{-1} \| X_i - X_j \|)\)` in the objective function with the refined kernel weight `\(K (h^{-1} \| \hat \beta_{\mathrm{opcg}}^\top (X_i - X_j) )\|)\)`. 3. Repeat 1 and 2 until `\(\hat \beta_{\mathrm{opcg}}\)` converges. The resulting estimator is the Refined OPCG estimator, `\(\hat \beta_{\mathrm{ropcg}}\)` -- The same iterative procedure for MADE produces the Refined MADE estimator, `\(\hat \beta_{\mathrm{rmade}}\)`. --- class: left, top # Estimating the dimension, `\(d\)` - Ladle plot and Predictor Augmentation methods are fast, eigen-based methods that can be applied to OPCG estimate `\(d\)`. (Luo and Li, 2020; Luo and Li, 2016) - Uses variation in eigenvectors and eigenvalues of `\(\hat \Lambda_{\mathrm{opcg}}\)` to determine `\(d\)`. - Cross-validation or sequential testing methods can be used to estimate `\(d\)` for MADE. (Adragni, 2018; Xia, Tong, Li, et al., 2002) --- class: inverse, center, middle  # .bg-text[Tuning the bandwidth] --- class: left, top # Tuning the bandwidth, `\(h\)`. .center[  ] The bandwidth `\(h\)` in the kernel `\(K(h^{-1} \|X_i - X_j \|)\)` determines the size of the local neighbourhoods about points `\(X_j\)`. This bandwidth needs to be tuned in our forward regression approaches. --- class: left, top # Tuning the bandwidth, `\(h\)`. We need to tune `\(h\)` in OPCG: - Cross Validation requires specifying a prediction method beforehand. - Can choose `\(h\)` according to optimal bandwidth, such as `\(h^{opt} = cn^{-\frac{1}{(p+6)}}\)` (Xia, 2007) - but suggested values of `\(c\)` does not always work, and then you need to tune `\(c\)`. -- For classification problems, we propose using a K-means clustering procedure for tuning `\(h\)`. Our intuition: - SDR should make classification easier, and classification is easiest when the dimension reduced predictors `\(\hat \beta^\top X\)` are clustered into their respective labels. --- class: left, top # Tuning the bandwidth, `\(h\)`. Let `\(Y \in \{1,...,m\}\)` be categorical response, `\((Y, X)_{1:n}\)` be our training set, `\((Y,X)_{1:n_2}^{\mathbb{V}}\)` be our validation set. -- Main idea: For each `\(h\)`, 1. Estimate `\(\hat \beta_{\mathrm{opcg}}\)` on training set and construct the sufficient predictors on the validation set, i.e. construct `\(\hat \beta_{\mathrm{opcg}}^\top X_{1:n_2}^{\mathbb{V}}\)`. 2. Apply K-means to sufficient predictors `\(\hat \beta_{\mathrm{opcg}}^\top X_{1:n_2}^{\mathbb{V}}\)` for `\(m\)` clusters. - This returns `\(m\)` estimated clusters and the F-ratio, i.e. the Within Sums-of-Squares (WSS) over Between Sums-of-Squares (BSS), for each `\(h\)`. 3. Select `\(h\)` that minimizes the F-ratio from K-means. - Small F-ratio means small WSS and large BSS --- class: left, top # Tuning the bandwidth, `\(h\)`. But - Estimating `\(m\)` clusters implicitly assumes we have only 1 cluster per class - we ought to incorporate the class/label information from `\(Y_{1:n}^{\mathbb{V}}\)`, when available. We modifiy K-means so that: 1. we can estimate more than 1 cluster per class; 2. k-means uses label information from `\(Y_{1:n}^{\mathbb{V}}\)`; a "Supervised" K-means **In place of a validation set, we apply our supervised k-means on the training set in a k-fold manner.** --- class: inverse, center, middle  # .bg-text[Simulations and Data Analyses] -- .left[ 1. Goal: generalize OPG to Categorical and Ordinal-Categorical Responses 2. Propose: OPCG that generalizes OPG to linear exponential families 3. Categorical and Ordinal-Categorical variables are linear exponential families, so OPCG we can apply OPCG ] --- class: left, top # OPCG Procedure Given data: 1. Simulations: we have 3 data sets - Training set used for estimating `\(\hat \beta_{opcg}\)`; - Used for estimating `\(d\)` as well. - Validation set used for tuning bandwidth `\(h\)` - Testing set used for assessing performance 2. Applications: Data is split into two sets: - Training set used for estimating `\(\hat \beta_{opcg}\)`; - Used for estimating `\(d\)` as well. - Used K-fold tuning for bandwidth `\(h\)` - Testing set used for assessing performance --- class: left, top # Simulations Our predictor will be `\(X=(X_1,X_2,X_3,...,X_{10}) \in \R^{10}\)`. - We generate generate 5 clusters from a bivariate normal `\((X_3, X_7)\)`, augmented with 8 standard normals. - Two clusters are labeled 1, two are labeled 2, and one cluster is labeled 3; So `\(Y \in \{1,2,3\}\)` is categorical. - We draw an equal number of observations from each cluster for the training, validation and testing set.        --- class: ani-slide # K-mean Tuning for `\(h\)`  --- count: false class: left, top # Simulations - tuning and estimation K-fold supervised K-means Tuning: `\(h \approx 1.26\)`;  -- - PL-OPCG estimates 2 SDR directions per binary logistic problem per class, for 6 total, and selects the that explain the most variation. - Lambert-Lacroix and Peyre's suggestion for multi-class problems. - PW-OPCG estimates 2 SDR directions per pair of classes \{1,2\}, \{1,3\}, and \{2,3\}, for 6 total, and selects the 2 that explain the most variation. - Adragni's suggestion for multi-class problems. - DR is Directional Regression (Li and Wang, 2007) --- count: false class: left, top # Simulations - Sufficient Predictors `\(\hat \beta^\top X^{test}\)` .center[   ] --- class: left, top # Ordinal-Categorical Data Analysis ## Red Wine Quality We a wine quality rating data set from the UCI repository: - Red Wine Quality - p=11; train/test: 1000/599; Ordinal response - Wine Quality Score;  --- class: left, top # Categorical Data Analysis We analyze three datasets with categorical responses: - Handwritten Digits (Pendigit) from UCI - p=16; train/test:=1000/1000; resp=0-9 - USPS Handwritten Digits - p=256; train/test:=1000/1007; resp=0-9 - ISOLET from UCI - p=618; train/test:=6334/1553; resp=a-z --- class: left, top # Categorical Classification Error using SVM .center[  ] --- class: ani-slide # Pen Digit 10 - SIR/DR - 1,6,7,9  --- count: false class: ani-slide # Pen Digit 10 - OPCG - 1,6,7,9  --- class: left, middle, inverse # .bg-text[Summary] 1. Provided the Multivariate Link Functions for Ordinal-Categorical Responses. 2. Generalized OPG to categorical and ordinal responses using multivariate GLMs 3. Introduced a supervised K-means tuning procedure for classification Paper Status: Preparing for submission --- class: inverse, left, middle # .center[.bg-text[Forward SDR for Categorical and Ordinal Response with Functional Predictors] ] 1. Local linear multivariate GLM with Functional Predictors 2. Tensor Product of Canonical Gradients --- class: top, left # Local linear multivariate GLM for Functional Predictors .center[   ] - `\(Y \in \R^m\)`, e.g. categorical or ordinal variable, - `\(X \in \mathscr{H}_X\)` a Reproducing Kernel Hilbert Space of functions, - `\(\partial \theta(x_0)^\top / \partial x \in \mathscr{B}(\mathscr{H}_X, \R^m)\)` is the Frechet Derivative at `\(x_0\)`, --- count:false class: top, left # Local linear multivariate GLM for Functional Predictors .center[   ] - We want a finite-rank linear operator `\(T \in \mathscr{B}(\mathscr{H}_X, \R^d)\)` such that `\(Y \indep E(Y|X) | T(X)\)` - the sufficient predictors are `\(T(X) = (\langle f_1, X \rangle_{\mathscr{H}_X},\ldots,\langle f_d, X \rangle_{\mathscr{H}_X}) \in \R^d\)` --- class: top, left # Progress Report 1. Existing methods in Literature: - Generalized Functional Linear Model (Müller and Stadtmüller, 2005; James, 2002; Li, Wang, and Carroll, 2010) - Fit local quasi-likelihood with functional predictors and euclidean predictors; no dimension reduction for functional predictors. (Li, Wang, and Carroll, 2010) 2. We estimate Local linear multivariate GLM with functional predictor by employing a RKHS using the "kernel method" 3. We develop an estimator for the range of `\(T^*\)`, which is a functional SDR subspace for `\(E(Y|X)\)`                      --- class: left, top # TPCG - UJI 3,5,9 UJI handwritten characters dataset (version 2) from UCI repository - 60 writers, 97 characters, 2 repetitions - 1st repetition is training set; 2nd repetition is testing set - 60 samples per character in training set and testing set - characters are 2d curves; - Goal: Classification of labels --- class: left, top # TPG - UJI 3,5,9 ## Classify digits 3,5,9: - predictor is 2d curves `\((X_1(t), X_2(t))\)` for '3', '5', '9'; - response is the categorical label: '3', '5', '9' ; .center[  ] --- class: left, top # TPCG - UJI 3,5,9 ## Classify digits 3,5,9: - predictor is 2d curve `\((X_1(t), X_2(t))\)` for '3', '5', '9'; - response is the categorical label: '3', '5', '9' ; .center[  ] --- class: left, top # Phoneme Data Phoneme dataset for speech Recognition from 'the Elements of Statistical Learning' text - five phonemes as responses: - 'sh' as in 'she', - 'dcl' as in 'dark', - 'iy' as in the vowel in 'she', - 'aa' as in the vowel in 'dark', - 'ao' as in the vowel in 'water'. - The dataset contains a total of 4509 speech recordings of 32 milliseconds. We compute a curve `\(X(t)\)` for each recording.  - We draw a training and testing set of 500 curves each. Each set is evenly divide between the phonemes. - Goal: Classification of labels --- class: left, top # Phoneme Data .center[  ] --- class: ani-slide # Phoneme Data  --- class: inverse, left, middle # .center[.bg-text[Forward SDR for Functional Response with Functional Predictors] ] 1. Local function-on-function linear regression 2. Tensor Product of Gradients --- class: top, left # Local Function-on-Function Linear Regression .center[   ] - `\(Y \in \mathscr{H}_Y\)` a Reproducing Kernel Hilbert Space of functions, - `\(X \in \mathscr{H}_X\)` a Reproducing Kernel Hilbert Space of functions, - `\(\partial E(Y|X)^\top / \partial x \in \mathscr{B}(\mathscr{H}_X, \mathscr{H}_Y)\)` is the Frechet Derivative, --- count:false class: top, left # Local Function-on-Function Linear Regression .center[   ] - We want a finite-rank linear operator `\(T \in \mathscr{B}(\mathscr{H}_X, \R^d)\)` such that `\(Y \indep E(Y|X) | T(X)\)` - the sufficient predictors are `\(T(X) = (\langle f_1, X \rangle_{\mathscr{H}_X},\ldots,\langle f_d, X \rangle_{\mathscr{H}_X}) \in \R^d\)` --- class: top, left # Progress Report 1. Existing methods in Literature: - Local scalar-on-function linear regression (Baillo and Grané, 2009; Ferraty and Nagy, 2021) - Ferraty and Nagy (2021) generalize ADE to scalar-on-function context 2. We estimate local function-on-function linear regression by using weighted covariance and cross-covariance operators in an RKHS 3. We have developed an estimator for the range of `\(T^*\)`, which is a functional SDR subspace for `\(E(Y|X)\)` --- class: left, top # TPG - UJI 3,5,9 to a,b,c UJI handwritten characters dataset (version 2) from UCI repository - 60 writers, 97 characters, 2 repetitions - 1st repetition is training set; 2nd repetition is testing set - 60 samples per character in training set and testing set - characters are 2d curves; - Goal: develop an association between characters --- class: left, top # TPG - UJI 3,5,9 to a,b,c ## Associate digits '3', '5', '9' to letters 'a', 'b', 'c': - predictor is 2d curves `\((X_1(t), X_2(t))\)` for '3', '5', '9'; - response is 2d curve `\((Y_1(t), Y_2(t))\)` for 'a', 'b', 'c'; .center[  ] --- class: left, top # TPG - UJI 3,5,9 to a,b,c ## Associate digits '3', '5', '9' to letters 'a', 'b', 'c': - predictor is 2d curves `\((X_1(t), X_2(t))\)` for '3', '5', '9'; - response is 2d curve `\((Y_1(t), Y_2(t))\)` for 'a', 'b', 'c'; .center[  ] --- class: top, left # Future Work ## (Immediate) Future work 1. Consistency of TPG and TPCG 2. Tuning TPG and TPCG 3. Estimating the dimension, `\(d\)`. 4. Generalizing MAVE and MADE to functional data 5. Simulations and additional Applications           --- layout: false # References Adragni, K. P. (2018). "Minimum average deviance estimation for sufficient dimension reduction". In: _Journal of Statistical Computation and Simulation_ 88.3, pp. 411-431. Agresti, A. (2010). _Analysis of ordinal categorical data_. Vol. 656. John Wiley & Sons. Baillo, A. and A. Grané (2009). "Local linear regression for functional predictor and scalar response". In: _Journal of Multivariate Analysis_ 100.1, pp. 102-111. Bura, E., B. Li, L. Li, et al. (2021). "A Conversation with Dennis Cook". In: _Statistical Science_ 36.2, pp. 328 - 337. DOI: [10.1214/20-STS801](https://doi.org/10.1214%2F20-STS801). URL: [https://doi.org/10.1214/20-STS801](https://doi.org/10.1214/20-STS801). Cook, R. D. and B. Li (2002). "Dimension reduction for conditional mean in regression". In: _The Annals of Statistics_ 30.2, pp. 455-474. Ferraty, F. and S. Nagy (2021). "Scalar-on-function local linear regression and beyond". In: _Biometrika_. asab027. ISSN: 0006-3444. DOI: [10.1093/biomet/asab027](https://doi.org/10.1093%2Fbiomet%2Fasab027). eprint: https://academic.oup.com/biomet/advance-article-pdf/doi/10.1093/biomet/asab027/37231257/asab027.pdf. URL: [https://doi.org/10.1093/biomet/asab027](https://doi.org/10.1093/biomet/asab027). --- layout: false # References Härdle, W. and T. M. Stoker (1989). "Investigating smooth multiple regression by the method of average derivatives". In: _Journal of the American statistical Association_ 84.408, pp. 986-995. Kim, K., B. Li, Z. Yu, et al. (2020). "On post dimension reduction statistical inference". In: _The Annals of Statistics_ 48.3, pp. 1567-1592. Kong, E. and Y. Xia (2012). "A single-index quantile regression model and its estimation". In: _Econometric Theory_ 28.4, pp. 730-768. Lambert-Lacroix, S. and J. Peyre (2006). "Local likelihood regression in generalized linear single-index models with applications to microarray data". In: _Computational statistics & data analysis_ 51.3, pp. 2091-2113. Lee, K., B. Li, and F. Chiaromonte (2013). "A general theory for nonlinear sufficient dimension reduction: Formulation and estimation". In: _The Annals of Statistics_ 41.1, pp. 221-249. Li, B. (2018). _Sufficient Dimension Reduction: Methods and Applications with R_. CRC Press. --- layout: false # References Lambert-Lacroix, S. and J. Peyre (2006). "Local likelihood regression in generalized linear single-index models with applications to microarray data". In: _Computational statistics & data analysis_ 51.3, pp. 2091-2113. Lee, K., B. Li, and F. Chiaromonte (2013). "A general theory for nonlinear sufficient dimension reduction: Formulation and estimation". In: _The Annals of Statistics_ 41.1, pp. 221-249. Li, B. (2018). _Sufficient Dimension Reduction: Methods and Applications with R_. CRC Press. Li, B. and J. Song (2017). "Nonlinear sufficient dimension reduction for functional data". In: _The Annals of Statistics_ 45.3, pp. 1059-1095. Li, B. and J. Song (2021). "Dimension Reduction For Functional Data Based on Weak Conditional Moments". In: _The Annals of Statistics_. Li, B. and S. Wang (2007). "On directional regression for dimension reduction". In: _Journal of the American Statistical Association_ 102.479, pp. 997-1008. --- layout: false # References Li, K. (1991). "Sliced inverse regression for dimension reduction". In: _Journal of the American Statistical Association_ 86.414, pp. 316-327. Li, Y., N. Wang, and R. J. Carroll (2010). "Generalized functional linear models with semiparametric single-index interactions". In: _Journal of the American Statistical Association_ 105.490, pp. 621-633. Luo, W. and B. Li (2016). "Combining eigenvalues and variation of eigenvectors for order determination". In: _Biometrika_ 103.4, pp. 875-887. Luo, W. and B. Li (2020). "On order determination by predictor augmentation". In: _Biometrika_. Luo, W., B. Li, and X. Yin (2014). "On efficient dimension reduction with respect to a statistical functional of interest". In: _The annals of statistics_ 42.1, pp. 382-412. Ma, Y. and L. Zhu (2012). "A semiparametric approach to dimension reduction". In: _Journal of the American Statistical Association_ 107.497, pp. 168-179.     <!-- PrintBibliography(c(bib_sdr, bib_opg, bib_funsdr), start=25) --> <!-- ``` -->


-
Initial Patient Population =
- AND: "Diagnosis, Active: Hospital Measures - AMI (ordinality: 'Principal')" starts during "Occurrence A of Encounter, Performed:
Encounter Inpatient"
- AND: "Patient Characteristic Birthdate: birth date" >= 18 year(s) starts before start of "Occurrence A of Encounter, Performed:
Encounter Inpatient"
- AND: "Occurrence A of Encounter, Performed: Encounter Inpatient (length of stay <= 120 day(s))"
- AND: "Occurrence A of Encounter, Performed: Encounter Inpatient" ends during "Measurement Period"
-
Denominator =
- AND: "Initial Patient Population"
-
Denominator Exclusions =
- AND:
- OR:
- AND: "Occurrence A of Encounter, Performed: Emergency Department Visit" <= 1 hour(s) ends before start of "Occurrence A of
Encounter, Performed: Encounter Inpatient"
- AND: "Occurrence A of Intervention, Performed: Comfort Measures" starts after start of "Occurrence A of Encounter, Performed:
Emergency Department Visit"
- OR:
- AND: "Occurrence A of Intervention, Performed: Comfort Measures" starts during "Occurrence A of Encounter, Performed: Encounter
Inpatient"
- AND NOT: "Occurrence A of Encounter, Performed: Emergency Department Visit" <= 1 hour(s) ends before start of "Occurrence
A of Encounter, Performed: Encounter Inpatient"
- OR:
- AND NOT: "Occurrence A of Patient Characteristic Clinical Trial Participant: Clinical Trial Participant" ends before start
of "Occurrence A of Encounter, Performed: Encounter Inpatient"
- AND: "Occurrence A of Patient Characteristic Clinical Trial Participant: Clinical Trial Participant" starts before or during
"Occurrence A of Encounter, Performed: Encounter Inpatient"
- OR:
- AND: "Occurrence A of Encounter, Performed: Emergency Department Visit" <= 1 hour(s) ends before start of "Occurrence A of
Encounter, Performed: Encounter Inpatient"
- AND NOT: "Occurrence A of Patient Characteristic Clinical Trial Participant: Clinical Trial Participant" ends before start
of "Occurrence A of Encounter, Performed: Emergency Department Visit"
- AND: "Occurrence A of Patient Characteristic Clinical Trial Participant: Clinical Trial Participant" starts before or during
"Occurrence A of Encounter, Performed: Emergency Department Visit"
- OR: "Occurrence A of Encounter, Performed: Encounter Inpatient (discharge status: 'Discharge To Another Hospital')"
- OR: "Occurrence A of Encounter, Performed: Encounter Inpatient (discharge status: 'Left Against Medical Advice')"
- OR: "Occurrence A of Encounter, Performed: Encounter Inpatient (discharge status: 'Discharged to Home for Hospice Care')"
- OR: "Occurrence A of Encounter, Performed: Encounter Inpatient (discharge status: 'Discharged to Health Care Facility for
Hospice Care')"
- OR: "Occurrence A of Encounter, Performed: Encounter Inpatient (discharge status: 'Patient Expired')"
-
Numerator =
- AND: "Medication, Discharge: Hospital Measures-Aspirin" starts during "Occurrence A of Encounter, Performed: Encounter Inpatient"
-
Denominator Exceptions =
- AND:
- OR:
- AND: "Medication, Administered not done: Hospital Measures - Hold" for "Hospital Measures-Aspirin " starts during "Occurrence
A of Encounter, Performed: Encounter Inpatient"
- AND NOT: "Occurrence A of Encounter, Performed: Emergency Department Visit" <= 1 hour(s) ends before start of "Occurrence
A of Encounter, Performed: Encounter Inpatient"
- OR:
- AND: "Occurrence A of Encounter, Performed: Emergency Department Visit" <= 1 hour(s) ends before start of "Occurrence A of
Encounter, Performed: Encounter Inpatient"
- AND: "Medication, Administered not done: Hospital Measures - Hold" for "Hospital Measures-Aspirin " starts after start of
"Occurrence A of Encounter, Performed: Emergency Department Visit"
- OR:
- OR: "Medication, Allergy: Aspirin Allergen"
- OR: "Medication, Order not done: Medical Reason" for "Aspirin ingredient specific "
- OR: "Medication, Discharge not done: Medical Reason" for "Aspirin ingredient specific "
- OR: "Medication, Order not done: Patient Refusal" for "Aspirin ingredient specific "
- OR: "Medication, Discharge not done: Patient Refusal" for "Aspirin ingredient specific "
- starts before or during "Occurrence A of Encounter, Performed: Encounter Inpatient"
- OR:
- OR: "Medication, Discharge: Warfarin"
- OR: "Medication, Discharge: Other Anticoagulants for AMI"
- starts during "Occurrence A of Encounter, Performed: Encounter Inpatient"
-
"Diagnosis, Active: Hospital Measures - AMI" using "Hospital Measures - AMI Grouping Value Set (2.16.840.1.113883.3.666.5.3011)"
-
"Encounter, Performed: Emergency Department Visit" using "Emergency Department Visit SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.292)"
-
"Encounter, Performed: Encounter Inpatient" using "Encounter Inpatient SNOMEDCT Value Set (2.16.840.1.113883.3.666.5.307)"
-
"Intervention, Performed: Comfort Measures " using "Comfort Measures SNOMEDCT Value Set (<IP_ADDRESS>.4.1.338<IP_ADDRESS>.45)"
-
"Medication, Administered not done: Hospital Measures - Hold" using "Hospital Measures - Hold SNOMEDCT Value Set (2.16.840.1.113883.3.666.5.1145)"
-
"Medication, Administered: Hospital Measures-Aspirin" using "Hospital Measures-Aspirin RXNORM Value Set (2.16.840.1.113883.3.666.5.626)"
-
"Medication, Allergy: Aspirin Allergen" using "Aspirin Allergen RXNORM Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.139)"
-
"Medication, Discharge not done: Medical Reason" using "Medical Reason SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.473)"
-
"Medication, Discharge not done: Patient Refusal" using "Patient Refusal SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.93)"
-
"Medication, Discharge: Aspirin ingredient specific" using "Aspirin ingredient specific RXNORM Value Set (2.16.840.1.1137<IP_ADDRESS>1.3)"
-
"Medication, Discharge: Hospital Measures-Aspirin" using "Hospital Measures-Aspirin RXNORM Value Set (2.16.840.1.113883.3.666.5.626)"
-
"Medication, Discharge: Other Anticoagulants for AMI" using "Other Anticoagulants for AMI RXNORM Value Set (2.16.840.1.1137<IP_ADDRESS>5.31)"
-
"Medication, Discharge: Warfarin" using "Warfarin RXNORM Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.232)"
-
"Medication, Order not done: Medical Reason" using "Medical Reason SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.473)"
-
"Medication, Order not done: Patient Refusal" using "Patient Refusal SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.93)"
-
"Medication, Order: Aspirin ingredient specific" using "Aspirin ingredient specific RXNORM Value Set (2.16.840.1.1137<IP_ADDRESS>1.3)"
-
"Patient Characteristic Birthdate: birth date" using "birth date LOINC Value Set (2.16.840.1.113883.3.560.100.4)"
-
"Patient Characteristic Clinical Trial Participant: Clinical Trial Participant" using "Clinical Trial Participant SNOMEDCT Value Set (2.16.840.1.113883.3.526.2.643)"
-
Attribute: "Discharge status: Discharge To Another Hospital" using "Discharge To Another Hospital SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.87)"
-
Attribute: "Discharge status: Discharged to Health Care Facility for Hospice Care" using "Discharged to Health Care Facility for Hospice Care SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.207)"
-
Attribute: "Discharge status: Discharged to Home for Hospice Care" using "Discharged to Home for Hospice Care SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.209)"
-
Attribute: "Ordinality: Principal" using "Principal SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.14)"
-
Attribute: "Discharge status: Patient Expired" using "Patient Expired SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.309)"
-
Attribute: "Discharge status: Left Against Medical Advice" using "Left Against Medical Advice SNOMEDCT Value Set (2.16.840.1.1138<IP_ADDRESS>.7.1.308)"
-
"Patient Characteristic Ethnicity: Ethnicity" using "Ethnicity CDCREC Value Set (2.16.840.1.114<IP_ADDRESS>7)"
-
"Patient Characteristic Payer: Payer" using "Payer SOP Value Set (2.16.840.1.114<IP_ADDRESS>91)"
-
"Patient Characteristic Race: Race" using "Race CDCREC Value Set (2.16.840.1.114<IP_ADDRESS>6)"
-
"Patient Characteristic Sex: ONC Administrative Sex" using "ONC Administrative Sex AdministrativeSex Value Set (2.16.840.1.1137<IP_ADDRESS>)"


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RapidLogLevel
public enum RapidLogLevel: Int
Logging level
- off: Logging is completely turned off
- critical: Show only critical messages e.g. reason of Rapid SDK intentional crash
- info: Show info about what is going on in Rapid SDK
- debug: Show info about what is going on in Rapid SDK along with I/O data description
-
Undocumented
Declaration
Swift
public enum RapidLogLevel: Int
-
Undocumented
Declaration
Swift
public enum RapidLogLevel: Int
-
Undocumented
Declaration
Swift
public enum RapidLogLevel: Int
-
Undocumented
Declaration
Swift
public enum RapidLogLevel: Int


[gecode-users] How to share living space object in Gecode 3.3.1
Guido Tack
tack at gecode.org
Thu May 27 09:26:30 CEST 2010
Vincent Barichard wrote:
> Before Gecode 3.3.1 I used the CopiedHandle to share an object between propagators in Space.
> The propagators stored useful information in this object (for example the number of "EqDom" propagators).
> These data are analyzed later in the branch-and-bound algorithm (which is a home-made algorithm).
>
> In Gecode 3.3.1 the CopiedHandle disappeared, so how could I share this object ? Maybe there is a simple way which I don't know yet ?
The memory management of CopiedHandle was completely broken and beyond repair, so we removed it. It may have worked for just exactly the case you used it for. I have just started implementing a replacement, it may become part of the next release (or the one after that).
For the time being, you could use a SharedHandle and always update it using false as the share argument (false here only means no sharing between spaces - the sharing within a space is always preserved!).
Would this be a workaround for your problem?
Cheers,
Guido
--
Guido Tack, http://people.cs.kuleuven.be/~guido.tack/
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{% extends 'admin/base_site.html' %} {% load static %} {% block content %}
The maximum daily downtime is {{ daily_statistics.max_downtime_minutes }} minutes.
The minimum daily uptime is {{ daily_statistics.min_uptime_percentage }}%.
The maximum daily downtime happened {{ daily_statistics.number_of_maximum_downtime }} time(s) , on {% for date in daily_statistics.dates_of_maximum_downtime %} {{date}} {% endfor %}
The average daily downtime is {{daily_statistics.average_downtime_minutes}}
The median of daily downtime is {{daily_statistics.median_downtime_minutes}}
The maximum monthly downtime is {{ monthly_statistics.max_downtime_minutes }} minutes.
The minimum monthly uptime is {{ monthly_statistics.min_uptime_percentage }}%.
The maximum monthly downtime happened {{ monthly_statistics.number_of_maximum_downtime }} time(s) , on {% for date in monthly_statistics.dates_of_maximum_downtime %} {{date}} {% endfor %}
The average monthly downtime is {{monthly_statistics.average_downtime_minutes}}
The median of monthly downtime is {{monthly_statistics.median_downtime_minutes}}


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Answer some questionnaires to make some pocket in your free time
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Provide a survey platform for people to easily get to access people and collect some data. It is not just a tool, but a brand new way for prople to get their ideas and thoughts shared through the world.
Our Plan
Create a brand new product to save the immediate cost of making a survey, and make it possible for budget-investigators such as students or social workers to deliver a survey easily.
Our Vision
In the near future, it is a new time of data and information explosion. That's where we see and that's where we are going, we will be the navigator of this ship to the new future of the information sharing new era.
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Publish totally customized quetionnaire with slight payment and collect the result in a short time
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Choose or search interested questionnaires to answer and the gain the credit to get paid
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Search and buy some already finishefd questionnaires results at a labeld price if don't want to publish a new ones
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The component-list
directive allows you to add or remove instances of the same component.
It should be given an ng-model
containing an array. If the array has any items components will initially be created for those values.
This array will be updated when a new field is added or when a component changes its model.
You should provide the html for the component you want to duplicate in a component
tag inside the component-list
directive.
Each component will have a property called model
available in its scope.
Your component should update this property which will update the ng-model
on the component-list
.
Use the following attributes to customize the behavior of the component:
ng-model
then the default value they will be given is null.


This website is dedicated to the prevention of cyberbullying and give resources for those who would like to learn more about cyber bullying and recieve help.


{% extends "layout_bsa.html" %} {% block page_title %} Help paying for NHS Costs prototype {% endblock %} {% block content %} Back Who is your employer? Employer's name Continue {% endblock %}


Module pl.test
Useful test utilities.
test.asserteq({1,2},{1,2}) -- can compare tables test.asserteq(1.2,1.19,0.02) -- compare FP numbers within precision T = test.tuple -- used for comparing multiple results test.asserteq(T(string.find(" me","me")),T(2,3))
Dependencies: `pl.utils`, `pl.tablex`, `pl.pretty`, `pl.path`, `debug`
Functions
| error_handler (file, line, got_text, needed_text, msg) | error handling for test results. |
| complain (x, y, msg, where) | general test complain message. |
| asserteq (x, y, eps, where) | like assert, except takes two arguments that must be equal and can be tables. |
| assertmatch (s1, s2, where) | assert that the first string matches the second. |
| assertraise (fn, e, where) | assert that the function raises a particular error. |
| asserteq2 (x1, x2, y1, y2, where) | a version of asserteq that takes two pairs of values. |
| tuple (...) | encode an arbitrary argument list as a tuple. |
| timer (msg, n, fun, ...) | Time a function. |
Functions
- error_handler (file, line, got_text, needed_text, msg)
-
error handling for test results.
By default, this writes to stderr and exits the program.
Re-define this function to raise an error and/or redirect output
Parameters:
- file
- line
- got_text
- needed_text
- msg
- complain (x, y, msg, where)
-
general test complain message.
Useful for composing new test functions (see tests/tablex.lua for an example)
Parameters:
- x a value
- y value to compare first value against
- msg message
- where extra level offset for errors
- asserteq (x, y, eps, where)
-
like assert, except takes two arguments that must be equal and can be tables.
If they are plain tables, it will use tablex.deepcompare.
Parameters:
- x any value
- y a value equal to x
- eps an optional tolerance for numerical comparisons
- where extra level offset
- assertmatch (s1, s2, where)
-
assert that the first string matches the second.
Parameters:
- s1 a string
- s2 a string
- where extra level offset
- assertraise (fn, e, where)
-
assert that the function raises a particular error.
Parameters:
- fn a function or a table of the form {function,arg1,...}
- e a string to match the error against
- where extra level offset
- asserteq2 (x1, x2, y1, y2, where)
-
a version of asserteq that takes two pairs of values.
x1==y1 and x2==y2
must be true. Useful for functions that naturally return two values.Parameters:
- x1 any value
- x2 any value
- y1 any value
- y2 any value
- where extra level offset
- tuple (...)
-
encode an arbitrary argument list as a tuple.
This can be used to compare to other argument lists, which is
very useful for testing functions which return a number of values.
Unlike regular array-like tables ('sequences') they may contain nils.
Tuples understand equality and know how to print themselves out.
The # operator is defined to be the size, irrespecive of any nils,
and there is an `unpack` method.
Parameters:
- ...
Usage:
asserteq(tuple( ('ab'):find 'a'), tuple(1,1))
- timer (msg, n, fun, ...)
-
Time a function. Call the function a given number of times, and report the number of seconds taken,
together with a message. Any extra arguments will be passed to the function.
Parameters:
- msg string a descriptive message
- n int number of times to call the function
- fun func the function
- ... optional arguments to fun


So far my experience with DBC has been both exciting, exhausting, and mind changing. Week one came at me at a really busy time during finals week, and has left me exhausted. I actually think it was a good learning experience as I got to work through a little mini crunch before experiencing the everyday fast pace at on site. I don't think I have ever learned so much in such a short period of time.
The video itself has relieved me of some nervousness, as I was definitely worried as to whether or not I would be under the curve coming into DBC. Shereef was very reassuring when he said everybody is at the 0.1% of mastering software engineering. His description of the attitude and work ethic is what I sort of expected from what I read about DBC as well as my experience with the interview and prep phase. The only expectations that have changed for me are my expectations of myself! I have always loved learning, but have been somewhat unmotivated putting What I have learned to use. This past day and a half I think I have proved to myself that I can accomplish everything I want to if I just put my mind to it.
I really cannot wait to set foot into DBC during phase 1. While I am somewhat nervous, I know that if I work at it I can really expand my skillset and become not only a better programmer but hopefully a better person.


category date element *exception exit limit note number part *property relation series text *transparent


java:
URI schemeARQ uses URIs of the form <java:package.class>
to provide
dynamic loading of code for value
functions and property functions.
ARQ loads the class when needed. For functions and property functions, it also
wraps it in the necessary factory code. A new instance of the function or
property function is created for each mention of the name in each query.
Any classes loaded by ARQ must already be on the java classpath. ARQ does not create any new class loaders, nor modify the Java class path in any way. The class path must be set up to include any class files or jar files for dynamically loaded code.
Classes can be mor conveniently named in queries using SPARQL PREFIX
es
but because dots can't appear in the local part of a prefixed name, all the
package name and the final dot must be in the PREFIX
declaration.
PREFIX fn: <java:org.example.functions.> # Including the final dot ... FILTER fn:alter(?x) ...
All code loading is performed via the MappedLoader
class. Before
actually loading the code, the mapped loader applies any transformation of URIs.
For example, the ARQ function library has a namespace of <http://jena.hpl.hp.com/ARQ/function#>
and resides in the Java package com.hp.hpl.jena.sparql.function.library. The
mapped loader includes a partial rewrite rule turning http URLs starting with
that namespace into java: URIs using the package name.


Current Train Schedule Train Name Destination Frequency (min) Next Arrival Minutes Away Add Train Name Destination First Train Frequency (min)


Financial Times names Khalaf editor after Lionel Barber ends 14-year stint
by Thomas SealLionel Barber will step down as editor of the Financial Times newspaper after 14 years in the post and will be replaced by Roula Khalaf, currently deputy editor, in January.
Barber joined in 1985 and helped steward the distinctively pink business paper through its 844 million pound (US$1.1 billion) purchase by Japanese publisher Nikkei Inc. in 2015 from education publisher Pearson Plc.
Khalaf will become one of the most senior women in global journalism. She’s been the FT’s deputy editor since 2016, having joined the paper in 1995 from Forbes magazine, the paper said in a statement. She has had stints as foreign editor and Middle East editor and has also driven initiatives to improve newsroom diversity.
The FT’s daily print circulation of 169,000 is less than half of what it was when Barber took the top job, according to the Audit Bureau of Circulations. Digital readership has swelled to a record of more one million paying customers across its digital platforms.
The paper posted operating profits of 25 million pounds on revenues of 383 million pounds in 2018, the company said. About 70 per cent of readers are outside the U.K.
In 2017 France awarded Barber the Legion d’honneur in 2017 in recognition of his contribution to journalism. The paper recently moved back to its historic headquarters, Bracken House, in the heart of London’s financial district.
Bloomberg competes with the Financial Times and Nikkei in business news publishing.


JackyRoll
You just got rickrolled by JackyBorderCollie!


To Whom It May Concern,
As a highly skilled digital educator with international experience
in marketing, business development, and startups, I am confident
that I will add value to your team, and fit in with your company
culture of continuous self-improvement, and moonshot thinking.
Naturally extroverted, I am at my best when speaking in front of
large groups. My presentations are dynamic, professional, and
highly persuasive. Read about
my win at The Next Web Hack Battle,
and watch my pitch to see me in action.
In my last position as International Marketing Manager for
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I managed over 20 promotional and sales staff. I truly
enjoy mentoring others, and setting high goals for success.
I hope we can delve further into my extensive skill set
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Kind regards,
Katie Williams, M.Ed.
Google Certified Teacher


Quiz: Can you guess the country from its lesser known cities?
In the pantheon of world cities, capitals hog all the attention. A few bits of paperwork and suddenly they get government, tourism, investment – and a lion’s share of quiz questions.
Here’s to those smaller metropolises that aren’t getting the love they deserve. Can you match the country to three of its lesser known cities?


Class: Aws::SageMaker::Types::ClarifyShapBaselineConfig
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb |
Overview
The configuration for the SHAP baseline (also called the background or reference dataset) of the Kernal SHAP algorithm.
ShapBaseline
andShapBaselineUri
are mutually exclusive parameters. One or the either is required to configure a SHAP baseline.
Constant Summary
Instance Attribute Summary
-
#mime_type ⇒ String
rw
The MIME type of the baseline data.
-
#shap_baseline ⇒ String
rw
The inline SHAP baseline data in string format.
-
#shap_baseline_uri ⇒ String
rw
The uniform resource identifier (URI) of the
::Aws::S3
bucket where the SHAP baseline file is stored.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Aws::Structure#to_h. |
Instance Attribute Details
#mime_type ⇒ String
(rw)
The MIME type of the baseline data. Choose from 'text/csv'
or
'application/jsonlines'
. Defaults to 'text/csv'
.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3702
class ClarifyShapBaselineConfig < Struct.new( :mime_type, :shap_baseline, :shap_baseline_uri) SENSITIVE = [] include Aws::Structure end
#shap_baseline ⇒ String
(rw)
The inline SHAP baseline data in string format. ShapBaseline
can
have one or multiple records to be used as the baseline dataset. The
format of the SHAP baseline file should be the same format as the
training dataset. For example, if the training dataset is in CSV
format and each record contains four features, and all features are
numerical, then the format of the baseline data should also share
these characteristics. For natural language processing (NLP) of text
columns, the baseline value should be the value used to replace the
unit of text specified by the Granularity
of the TextConfig
parameter. The size limit for ShapBasline
is 4 KB. Use the
ShapBaselineUri
parameter if you want to provide more than 4 KB of
baseline data.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3702
class ClarifyShapBaselineConfig < Struct.new( :mime_type, :shap_baseline, :shap_baseline_uri) SENSITIVE = [] include Aws::Structure end
#shap_baseline_uri ⇒ String
(rw)
The uniform resource identifier (URI) of the ::Aws::S3
bucket where the
SHAP baseline file is stored. The format of the SHAP baseline file
should be the same format as the format of the training dataset. For
example, if the training dataset is in CSV format, and each record
in the training dataset has four features, and all features are
numerical, then the baseline file should also have this same format.
Each record should contain only the features. If you are using a
virtual private cloud (VPC), the ShapBaselineUri
should be
accessible to the VPC. For more information about setting up
endpoints with Amazon Virtual Private Cloud, see Give ::Aws::SageMaker
access to ::Aws::Resources
in your Amazon Virtual Private Cloud.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3702
class ClarifyShapBaselineConfig < Struct.new( :mime_type, :shap_baseline, :shap_baseline_uri) SENSITIVE = [] include Aws::Structure end


---
layout: post
title: Hello World Program With Python
---
Today we are going to learn how to make a basic 'Hello World!' program with python.
Prerequisites
Python2.7+
Any text editor will do
To begin, open your text editor and type the following at the top of the page:
#!/usr/bin/env python
This tells our computer that we will be using Python to compile this program.
Python is complied at run time so there is no need to precompile the file to get it to
work, just make sure you have this header.
Now we will add:
print "Hello World!"
If you are using Python3+, your syntax should be:
print("Hello World")
Because 'print' was changed to a function in Python3
Save your file as helloworld.py and exit your editor
Now go to your terminal, navigate to the directory where your file is saved, and type:
python helloworld.py
You should see something like this...
That's it for this tutorial. Stay tuned for more awesome python tutorials.
While your waiting for the next one to be released, check out some of the other tutorials
on our site
Author: 73RM1N41@Program13


I work every day and it always sounds important, but it's not very important. It's mostly made up stuff for people to worry about. They're all pretty concerned.
Sometimes i like to do these things
You can reach me via linkedin. myLinkedIn


Exact segmentation: Pelt¶
Description¶
The method is implemented in ruptures.detection.Pelt
.
Because the enumeration of all possible partitions impossible, the algorithm relies on a pruning rule. Many indexes are discarded, greatly reducing the computational cost while retaining the ability to find the optimal segmentation. The implementation follows [BKFE12]. In addition, under certain conditions on the change point repartition, the computational complexity is linear on average.
When calling ruptures.detection.Pelt.__init__()
, the minimum distance between change points
can be set through the keyword 'min_size'
; through the parameter 'jump'
, only change
point indexes multiple of a particular value are considered.
Usage¶
import numpy as np
import matplotlib.pylab as plt
import ruptures as rpt
# creation of data
n, dim = 500, 3
n_bkps, sigma = 3, 1
signal, b = rpt.pw_constant(n, dim, n_bkps, noise_std=sigma)
# change point detection
model = "l1" # "l2", "rbf"
algo = rpt.Pelt(model=model, min_size=3, jump=5).fit(signal)
my_bkps = algo.predict(pen=3)
# show results
fig, (ax,) = rpt.display(signal, bkps, my_bkps, figsize=(10, 6))
plt.show()
Code explanation¶
-
class
ruptures.detection.
Pelt
(model='l2', custom_cost=None, min_size=2, jump=5, params=None)[source]¶ Penalized change point detection.
For a given model and penalty level, computes the segmentation which minimizes the constrained sum of approximation errors.
-
__init__
(model='l2', custom_cost=None, min_size=2, jump=5, params=None)[source]¶ Initialize a Pelt instance.
- Parameters
model (str, optional) – segment model, [“l1”, “l2”, “rbf”]. Not used if
'custom_cost'
is not None.custom_cost (BaseCost, optional) – custom cost function. Defaults to None.
min_size (int, optional) – minimum segment length.
jump (int, optional) – subsample (one every jump points).
params (dict, optional) – a dictionary of parameters for the cost instance.
- Returns
self
-
fit
(signal)[source]¶ Set params.
- Parameters
signal (array) – signal to segment. Shape (n_samples, n_features) or (n_samples,).
- Returns
self
-
References
- BKFE12
R. Killick, P. Fearnhead, and I. Eckley. Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500):1590–1598, 2012.


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Length: {{ length }}
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({{$t('examples')}}: 平
| 上
| 去
| 入
| 仄
| 陽平
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Distribution of final note melodies
Notes following notes
Skeletal:
All contours (for "{{ toneQuery }}")
All contours (for words with 2 or more notes)
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({{$t('examples')}}: 。
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Total word counts across all lines
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Average rhythmic time: {{ matchedToneQuarters.toFixed(2) }} quarters
Average # of quarter beats, by line position
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Average # of notes per character, by line position
Tones of rhyming characters
(平 = 0, 上 = 1, 去 = 2, 入 = 3)
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(平 = 0, 上 = 1, 去 = 2, 入 = 3)
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What are the benefits of Color Therapy?
Color Therapy can releave stress and be an overall great tool for stress relief and coping with mental health disorders.
Why do you do what you do?
We do what we do to glorify God and glorify others.
Do you plan on opening more locations?
Potentially in the near future, we plan to open locations in New York, California, and Virginia.


A Victorious Union
by Oliver Optic
Chapter XXVI: “The St. Regis in Commission”
Additional Information
- Year Published: 1893
- Language: English
- Country of Origin: United States of America
- Source: Optic, O. (1893). A Victorious Union. Boston, MA: Lee and Shepherd Publishers.
-
Readability:
- Flesch–Kincaid Level: 9.0
- Word Count: 2,201
- Genre: Adventure
- Keywords: 19th century literature, american literature, blue and the gray, civil war
- ✎ Cite This
Downloads
The kindly expressions of feeling which passed between the hosts and their guests were far from being mere compliments, for the Confederate commander and surgeon had made themselves very agreeable. Quite a number of pleasant parties had been given in compliment to them and Christy. But the family felt that they owed a debt of gratitude to their guests which they could not repay; and enemies though they were, the most eminent personages on the Federal side could not have been better treated.
“I am sorry you are going, though I congratulate you on the prospect now before you of returning to your friends,” said Captain Passford, after the conversation had continued for half an hour. “But I did not come in to receive your adieus; only to introduce to you, and to Mrs. Passford and Florry, a new character, who has just stepped upon the stage of action.”
“Draw it mild, papa,” interposed Christy, shrugging his shoulders.
“I have the pleasure of presenting to you Lieutenant-Commander Christopher Passford.”
Captain Rombold and Dr. Davidson set to clapping their hands as though they had suddenly gone crazy. When the former had nearly blistered his own, he rushed to the newly-promoted, and grasped his hands with a pressure which made the recipient of his warm greeting squirm with pain.
“I congratulate you with all my heart and mind, Commander Passford,” he added, with exceeding warmth. “I know that you deserved this promotion, and I was sure you would get it from the moment I saw you in the mizzen rigging of the Bellevite, and within the same minute leaping over the rail of the Tallahatchie, closely followed by thirty or forty of your seamen. I lost all hope of taking your ship then, for almost at the same instant came the discharge of the thirty-pounder I had prepared to lay low half your boarders. I told you this would come, but you seemed to be doubtful of it; and I repeat what I have said before, that God makes some fully-developed men before they are twenty-one.”
The surgeon followed the example of his fellow-prisoner; and then Christy’s mother and sister hugged and kissed him, and he heartily returned their affectionate embraces.
“I have only to add that my son has been appointed to the command of the St. Regis, a steamer of over eight hundred tons, and reputed to have a speed of twenty knots an hour, though I have some doubts in regard to the last item,” said Captain Passford.
“I cannot wish him success in his new command, for that would be treason; but I have no doubt he will damage our cause even more than he has in the past; and so far as he is personally concerned, I can wish him success with all my heart,” added Captain Rombold. “I have kept a list of the names of the vessels in the Federal navy so far as I could obtain them; but it does not include the St.— What you call her? I never heard the name before.”
“The St. Regis, after a river in the Adirondacks,” said Captain Passford, laughing. “But I can assure you, Captain, that you know her better than any of the rest of us, for I never even saw her.”
“The St. Regis?” interrogated the commander, puzzled by the assertion.
“Just now this steamer is something like a newly-married widow, for she is entering upon her third name,” continued the host, very lightly. "Formerly she was the Trafalgar, a highly honored name in British history; but more recently she received the name of Tallahatchie; and now she becomes the St. Regis.”
“I see,” replied the Confederate commander, evidently trying to hide his intense chagrin that the magnificent steamer, purchased by Colonel Homer Passford for him, had so soon become a ship belonging to the Federal navy. “You expressed a doubt in regard to her speed, my dear Captain.”
“I simply doubted if she could make twenty knots an hour, for the Bellevite overhauled her without difficulty.”
“That was because our coal was very bad. The Trafalgar made twenty knots an hour several times when she was under my command.”
“So much the better, Captain; if the speed is in her, her new engineer will get it out of her,” replied the host. “But I must take the next train for New York, and I am going over to see the St. Regis, for she has been put in the best of repair. Perhaps you would like to go with me, Christy.”
“I should, father; I was expecting Charley Graines over this morning, and he would like to see his future home on the deep,” replied the lieutenant-commander.
“He is in the reception-room now, waiting to see you,” said Florry.
“I have his appointment in my pocket, and you may give it to him, my son,” added the captain.
The guests were not to leave at once, and the trio hastened to the train. As soon as they were seated, Christy gave his friend the envelope containing his appointment, and Charley Graines was quite as happy as the future commander of the St. Regis. On the way the latter gave the other all the news that had come out that morning.
“I suppose Paul Vapoor will not come on board till we get to the Gulf, father,” said Christy.
“You will receive your orders to-morrow, as you have been advised; and though I cannot properly inform you where you will be bound, I can tell you where you are not bound; you are not going to the Gulf of Mexico,” answered Captain Passford.
“Not to the Gulf? All my service so far in blockaders has been in the Gulf, and this will be a tremendous change for me. But where shall we pick up our chief engineer?”
“About all the business growing out of the capture of the Tallahatchie, including the promotions, was done very nearly four weeks ago. I was in Washington when Captain Breaker’s very full report came, and the officers were promoted then. The appointments were also made then; but I have been obliged, for reasons not necessary to be named, to keep them to myself. The steamer that carried a cargo of coal, provisions, and stores to the Eastern Gulf squadron, was the bearer of Paul’s appointment to the St. Regis, and Mr. Bolter’s commission as chief engineer of the Bellevite. Your friend was ordered to report at the Brooklyn Navy Yard at once. The steamer in which he came put in at Delaware Breakwater, short of coal. He will be here by to-morrow morning, or sooner.”
After a visit at his office Captain Passford and his companions proceeded to the navy yard. The St. Regis was off the shore at anchor. She was a magnificent steamer; and the captain indulged in an exclamation, which he seldom did, when she was pointed out to him. She was all ready for sea, and would go into commission as soon as her commander presented himself. They went on board of her, and were heartily welcomed by such officers as had already occupied their staterooms.
Captain Passford went all over her, accompanied by Christy, while the new first assistant engineer confined his attention to the engine. The lieutenant-commander informed the proper officer of the yard that he would hoist the flag on board of the St. Regis at noon the next day. The party took their leave, and in the afternoon returned to Bonnydale.
The guests were now relieved from their parole, and they took their leave before night, with a repetition of the good wishes which had been expressed before. The next morning Christy was at the railroad station on the arrival of the train from New York, and the first person that rushed into his arms like a school-girl was Paul Vapoor. Of course Christy was delighted to see him, but he kept watching the steps of the principal car all the time. At last he discovered Bertha Pembroke, and he rushed to her, leaving Paul talking into the air.
He grasped the beautiful maiden by both hands, and both of them blushed like a carnation pink. The young officer was not given to demonstrations in public, and he reserved them to a more suitable occasion. He picked up her hand-bag and bundles which she had dropped when the lover took possession of her, and conducted her to his father’s carriage.
Christy presented her to Paul, who had heard much about her, but had never seen her. He was simply polite, though there was mischief in his eye, and the commander was in danger of being teased very nicely when they were alone together. Both Bertha and Paul were cordially welcomed by Mrs. Passford and Florry, and Christy needed nothing more to complete his happiness.
But there was no time to spare, and Captain Passford hurried them without mercy, and without considering that the lovers had not met before for several months; but the commander of the St. Regis was to hoist his flag at noon, and there was no room for long speeches. Christy and Paul hurried themselves into their new uniforms, not made for the occasion, but kept in store. The engineer’s uniform was all right as it was, for he had before reached the top of the ladder in his profession, but Flurry had changed the shoulder-straps of her brother.
Captain Passford was not remorseless in separating the newly reunited friends; for Paul and Flora had done some blushing, and had crept away into a corner of the great drawing-room as soon as he had put on his best uniform, and he finally insisted that all the ladies should go to the navy yard and witness the ceremony. The company were rather late; but the captain had sent a man to the station in advance, and the train was held for them.
It is hardly necessary to state in what manner the seats in the car were occupied; but the captain and Mrs. Passford had to sit together. A navy yard tugboat was at the foot of Grand Street on the arrival of the party, for it had been telegraphed for early in the morning. Captain Passford was a very distinguished magnate in the eyes of all naval officers, not only on account of his great wealth, but because he was the most influential man in the city at the department.
Half an hour before the time the party were on the deck of the St. Regis. All the officers were now on board; and while Paul was showing the ladies over the vessel, the commander was renewing his acquaintance with Mr. Baskirk, the executive officer. His father introduced Mr. Makepeace to him; and he found him a sturdy old salt, without as much polish as many of the officers, but a gentleman in every respect.
“I am very glad to know you, Captain Passford,” said Mr. Makepeace. “We have one of the most brilliant commanders in the service, and I suppose he will make things hum on board of the St. Regis, if we get into action, as we are likely to do under his lead.”
“I shall try to do my whole duty, and I shall endeavor not to make any sensation about it,” replied Christy, as he turned from the second to greet the third lieutenant, Mr. Drake, who had been his shipmate on board of the Bellevite, and the commander of the Tallahatchie while he was a passenger on board.
The ship’s company had already been mustered on deck. They were dressed in their best uniforms, and they were a fine-looking set of men. They had all heard of Lieutenant Passford, and they were proud and happy to serve under his command. Promptly at noon, as the church bells on shore were striking the hour, Commander Passford mounted a dais, and his commission was read to the ship’s company. He then made a short speech suited to the occasion, and ordered the colors to be run up to the peak. The ship was then in commission, and she was to sail on the tide the next day. The subordinate officers and seamen then gave three cheers, in which every person seemed to put his whole heart.
Christy conducted Bertha to the captain’s cabin, which had been restored to its original condition and refurnished. A lunch was served to the whole party under an awning on the quarter-deck. Mr. Drake, an eye-witness and actor in the battle, fought it over for the benefit of the ladies; and before night they all returned to Bonnydale, where it required at least three rooms to accommodate them during the evening.