참고
Click here to download the full example code
(beta) Building a Simple CPU Performance Profiler with FX¶
Author: James Reed
In this tutorial, we are going to use FX to do the following:
Capture PyTorch Python code in a way that we can inspect and gather statistics about the structure and execution of the code
Build out a small class that will serve as a simple performance 《profiler》, collecting runtime statistics about each part of the model from actual runs.
For this tutorial, we are going to use the torchvision ResNet18 model for demonstration purposes.
import torch
import torch.fx
import torchvision.models as models
rn18 = models.resnet18()
rn18.eval()
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=1000, bias=True)
)
Now that we have our model, we want to inspect deeper into its performance. That is, for the following invocation, which parts of the model are taking the longest?
input = torch.randn(5, 3, 224, 224)
output = rn18(input)
A common way of answering that question is to go through the program source, add code that collects timestamps at various points in the program, and compare the difference between those timestamps to see how long the regions between the timestamps take.
That technique is certainly applicable to PyTorch code, however it would be nicer if we didn’t have to copy over model code and edit it, especially code we haven’t written (like this torchvision model). Instead, we are going to use FX to automate this 《instrumentation》 process without needing to modify any source.
First, let’s get some imports out of the way (we will be using all of these later in the code).
import statistics, tabulate, time
from typing import Any, Dict, List
from torch.fx import Interpreter
참고
tabulate
is an external library that is not a dependency of PyTorch.
We will be using it to more easily visualize performance data. Please
make sure you’ve installed it from your favorite Python package source.
Capturing the Model with Symbolic Tracing¶
Next, we are going to use FX’s symbolic tracing mechanism to capture the definition of our model in a data structure we can manipulate and examine.
traced_rn18 = torch.fx.symbolic_trace(rn18)
print(traced_rn18.graph)
graph():
%x : torch.Tensor [#users=1] = placeholder[target=x]
%conv1 : [#users=1] = call_module[target=conv1](args = (%x,), kwargs = {})
%bn1 : [#users=1] = call_module[target=bn1](args = (%conv1,), kwargs = {})
%relu : [#users=1] = call_module[target=relu](args = (%bn1,), kwargs = {})
%maxpool : [#users=2] = call_module[target=maxpool](args = (%relu,), kwargs = {})
%layer1_0_conv1 : [#users=1] = call_module[target=layer1.0.conv1](args = (%maxpool,), kwargs = {})
%layer1_0_bn1 : [#users=1] = call_module[target=layer1.0.bn1](args = (%layer1_0_conv1,), kwargs = {})
%layer1_0_relu : [#users=1] = call_module[target=layer1.0.relu](args = (%layer1_0_bn1,), kwargs = {})
%layer1_0_conv2 : [#users=1] = call_module[target=layer1.0.conv2](args = (%layer1_0_relu,), kwargs = {})
%layer1_0_bn2 : [#users=1] = call_module[target=layer1.0.bn2](args = (%layer1_0_conv2,), kwargs = {})
%add : [#users=1] = call_function[target=operator.add](args = (%layer1_0_bn2, %maxpool), kwargs = {})
%layer1_0_relu_1 : [#users=2] = call_module[target=layer1.0.relu](args = (%add,), kwargs = {})
%layer1_1_conv1 : [#users=1] = call_module[target=layer1.1.conv1](args = (%layer1_0_relu_1,), kwargs = {})
%layer1_1_bn1 : [#users=1] = call_module[target=layer1.1.bn1](args = (%layer1_1_conv1,), kwargs = {})
%layer1_1_relu : [#users=1] = call_module[target=layer1.1.relu](args = (%layer1_1_bn1,), kwargs = {})
%layer1_1_conv2 : [#users=1] = call_module[target=layer1.1.conv2](args = (%layer1_1_relu,), kwargs = {})
%layer1_1_bn2 : [#users=1] = call_module[target=layer1.1.bn2](args = (%layer1_1_conv2,), kwargs = {})
%add_1 : [#users=1] = call_function[target=operator.add](args = (%layer1_1_bn2, %layer1_0_relu_1), kwargs = {})
%layer1_1_relu_1 : [#users=2] = call_module[target=layer1.1.relu](args = (%add_1,), kwargs = {})
%layer2_0_conv1 : [#users=1] = call_module[target=layer2.0.conv1](args = (%layer1_1_relu_1,), kwargs = {})
%layer2_0_bn1 : [#users=1] = call_module[target=layer2.0.bn1](args = (%layer2_0_conv1,), kwargs = {})
%layer2_0_relu : [#users=1] = call_module[target=layer2.0.relu](args = (%layer2_0_bn1,), kwargs = {})
%layer2_0_conv2 : [#users=1] = call_module[target=layer2.0.conv2](args = (%layer2_0_relu,), kwargs = {})
%layer2_0_bn2 : [#users=1] = call_module[target=layer2.0.bn2](args = (%layer2_0_conv2,), kwargs = {})
%layer2_0_downsample_0 : [#users=1] = call_module[target=layer2.0.downsample.0](args = (%layer1_1_relu_1,), kwargs = {})
%layer2_0_downsample_1 : [#users=1] = call_module[target=layer2.0.downsample.1](args = (%layer2_0_downsample_0,), kwargs = {})
%add_2 : [#users=1] = call_function[target=operator.add](args = (%layer2_0_bn2, %layer2_0_downsample_1), kwargs = {})
%layer2_0_relu_1 : [#users=2] = call_module[target=layer2.0.relu](args = (%add_2,), kwargs = {})
%layer2_1_conv1 : [#users=1] = call_module[target=layer2.1.conv1](args = (%layer2_0_relu_1,), kwargs = {})
%layer2_1_bn1 : [#users=1] = call_module[target=layer2.1.bn1](args = (%layer2_1_conv1,), kwargs = {})
%layer2_1_relu : [#users=1] = call_module[target=layer2.1.relu](args = (%layer2_1_bn1,), kwargs = {})
%layer2_1_conv2 : [#users=1] = call_module[target=layer2.1.conv2](args = (%layer2_1_relu,), kwargs = {})
%layer2_1_bn2 : [#users=1] = call_module[target=layer2.1.bn2](args = (%layer2_1_conv2,), kwargs = {})
%add_3 : [#users=1] = call_function[target=operator.add](args = (%layer2_1_bn2, %layer2_0_relu_1), kwargs = {})
%layer2_1_relu_1 : [#users=2] = call_module[target=layer2.1.relu](args = (%add_3,), kwargs = {})
%layer3_0_conv1 : [#users=1] = call_module[target=layer3.0.conv1](args = (%layer2_1_relu_1,), kwargs = {})
%layer3_0_bn1 : [#users=1] = call_module[target=layer3.0.bn1](args = (%layer3_0_conv1,), kwargs = {})
%layer3_0_relu : [#users=1] = call_module[target=layer3.0.relu](args = (%layer3_0_bn1,), kwargs = {})
%layer3_0_conv2 : [#users=1] = call_module[target=layer3.0.conv2](args = (%layer3_0_relu,), kwargs = {})
%layer3_0_bn2 : [#users=1] = call_module[target=layer3.0.bn2](args = (%layer3_0_conv2,), kwargs = {})
%layer3_0_downsample_0 : [#users=1] = call_module[target=layer3.0.downsample.0](args = (%layer2_1_relu_1,), kwargs = {})
%layer3_0_downsample_1 : [#users=1] = call_module[target=layer3.0.downsample.1](args = (%layer3_0_downsample_0,), kwargs = {})
%add_4 : [#users=1] = call_function[target=operator.add](args = (%layer3_0_bn2, %layer3_0_downsample_1), kwargs = {})
%layer3_0_relu_1 : [#users=2] = call_module[target=layer3.0.relu](args = (%add_4,), kwargs = {})
%layer3_1_conv1 : [#users=1] = call_module[target=layer3.1.conv1](args = (%layer3_0_relu_1,), kwargs = {})
%layer3_1_bn1 : [#users=1] = call_module[target=layer3.1.bn1](args = (%layer3_1_conv1,), kwargs = {})
%layer3_1_relu : [#users=1] = call_module[target=layer3.1.relu](args = (%layer3_1_bn1,), kwargs = {})
%layer3_1_conv2 : [#users=1] = call_module[target=layer3.1.conv2](args = (%layer3_1_relu,), kwargs = {})
%layer3_1_bn2 : [#users=1] = call_module[target=layer3.1.bn2](args = (%layer3_1_conv2,), kwargs = {})
%add_5 : [#users=1] = call_function[target=operator.add](args = (%layer3_1_bn2, %layer3_0_relu_1), kwargs = {})
%layer3_1_relu_1 : [#users=2] = call_module[target=layer3.1.relu](args = (%add_5,), kwargs = {})
%layer4_0_conv1 : [#users=1] = call_module[target=layer4.0.conv1](args = (%layer3_1_relu_1,), kwargs = {})
%layer4_0_bn1 : [#users=1] = call_module[target=layer4.0.bn1](args = (%layer4_0_conv1,), kwargs = {})
%layer4_0_relu : [#users=1] = call_module[target=layer4.0.relu](args = (%layer4_0_bn1,), kwargs = {})
%layer4_0_conv2 : [#users=1] = call_module[target=layer4.0.conv2](args = (%layer4_0_relu,), kwargs = {})
%layer4_0_bn2 : [#users=1] = call_module[target=layer4.0.bn2](args = (%layer4_0_conv2,), kwargs = {})
%layer4_0_downsample_0 : [#users=1] = call_module[target=layer4.0.downsample.0](args = (%layer3_1_relu_1,), kwargs = {})
%layer4_0_downsample_1 : [#users=1] = call_module[target=layer4.0.downsample.1](args = (%layer4_0_downsample_0,), kwargs = {})
%add_6 : [#users=1] = call_function[target=operator.add](args = (%layer4_0_bn2, %layer4_0_downsample_1), kwargs = {})
%layer4_0_relu_1 : [#users=2] = call_module[target=layer4.0.relu](args = (%add_6,), kwargs = {})
%layer4_1_conv1 : [#users=1] = call_module[target=layer4.1.conv1](args = (%layer4_0_relu_1,), kwargs = {})
%layer4_1_bn1 : [#users=1] = call_module[target=layer4.1.bn1](args = (%layer4_1_conv1,), kwargs = {})
%layer4_1_relu : [#users=1] = call_module[target=layer4.1.relu](args = (%layer4_1_bn1,), kwargs = {})
%layer4_1_conv2 : [#users=1] = call_module[target=layer4.1.conv2](args = (%layer4_1_relu,), kwargs = {})
%layer4_1_bn2 : [#users=1] = call_module[target=layer4.1.bn2](args = (%layer4_1_conv2,), kwargs = {})
%add_7 : [#users=1] = call_function[target=operator.add](args = (%layer4_1_bn2, %layer4_0_relu_1), kwargs = {})
%layer4_1_relu_1 : [#users=1] = call_module[target=layer4.1.relu](args = (%add_7,), kwargs = {})
%avgpool : [#users=1] = call_module[target=avgpool](args = (%layer4_1_relu_1,), kwargs = {})
%flatten : [#users=1] = call_function[target=torch.flatten](args = (%avgpool, 1), kwargs = {})
%fc : [#users=1] = call_module[target=fc](args = (%flatten,), kwargs = {})
return fc
This gives us a Graph representation of the ResNet18 model. A Graph
consists of a series of Nodes connected to each other. Each Node
represents a call-site in the Python code (whether to a function,
a module, or a method) and the edges (represented as args
and kwargs
on each node) represent the values passed between these call-sites. More
information about the Graph representation and the rest of FX’s APIs ca
be found at the FX documentation https://pytorch.org/docs/master/fx.html.
Creating a Profiling Interpreter¶
Next, we are going to create a class that inherits from torch.fx.Interpreter
.
Though the GraphModule
that symbolic_trace
produces compiles Python code
that is run when you call a GraphModule
, an alternative way to run a
GraphModule
is by executing each Node
in the Graph
one by one. That is
the functionality that Interpreter
provides: It interprets the graph node-
by-node.
By inheriting from Interpreter
, we can override various functionality and
install the profiling behavior we want. The goal is to have an object to which
we can pass a model, invoke the model 1 or more times, then get statistics about
how long the model and each part of the model took during those runs.
Let’s define our ProfilingInterpreter
class:
class ProfilingInterpreter(Interpreter):
def __init__(self, mod : torch.nn.Module):
# Rather than have the user symbolically trace their model,
# we're going to do it in the constructor. As a result, the
# user can pass in any ``Module`` without having to worry about
# symbolic tracing APIs
gm = torch.fx.symbolic_trace(mod)
super().__init__(gm)
# We are going to store away two things here:
#
# 1. A list of total runtimes for ``mod``. In other words, we are
# storing away the time ``mod(...)`` took each time this
# interpreter is called.
self.total_runtime_sec : List[float] = []
# 2. A map from ``Node`` to a list of times (in seconds) that
# node took to run. This can be seen as similar to (1) but
# for specific sub-parts of the model.
self.runtimes_sec : Dict[torch.fx.Node, List[float]] = {}
######################################################################
# Next, let's override our first method: ``run()``. ``Interpreter``'s ``run``
# method is the top-level entry point for execution of the model. We will
# want to intercept this so that we can record the total runtime of the
# model.
def run(self, *args) -> Any:
# Record the time we started running the model
t_start = time.time()
# Run the model by delegating back into Interpreter.run()
return_val = super().run(*args)
# Record the time we finished running the model
t_end = time.time()
# Store the total elapsed time this model execution took in the
# ``ProfilingInterpreter``
self.total_runtime_sec.append(t_end - t_start)
return return_val
######################################################################
# Now, let's override ``run_node``. ``Interpreter`` calls ``run_node`` each
# time it executes a single node. We will intercept this so that we
# can measure and record the time taken for each individual call in
# the model.
def run_node(self, n : torch.fx.Node) -> Any:
# Record the time we started running the op
t_start = time.time()
# Run the op by delegating back into Interpreter.run_node()
return_val = super().run_node(n)
# Record the time we finished running the op
t_end = time.time()
# If we don't have an entry for this node in our runtimes_sec
# data structure, add one with an empty list value.
self.runtimes_sec.setdefault(n, [])
# Record the total elapsed time for this single invocation
# in the runtimes_sec data structure
self.runtimes_sec[n].append(t_end - t_start)
return return_val
######################################################################
# Finally, we are going to define a method (one which doesn't override
# any ``Interpreter`` method) that provides us a nice, organized view of
# the data we have collected.
def summary(self, should_sort : bool = False) -> str:
# Build up a list of summary information for each node
node_summaries : List[List[Any]] = []
# Calculate the mean runtime for the whole network. Because the
# network may have been called multiple times during profiling,
# we need to summarize the runtimes. We choose to use the
# arithmetic mean for this.
mean_total_runtime = statistics.mean(self.total_runtime_sec)
# For each node, record summary statistics
for node, runtimes in self.runtimes_sec.items():
# Similarly, compute the mean runtime for ``node``
mean_runtime = statistics.mean(runtimes)
# For easier understanding, we also compute the percentage
# time each node took with respect to the whole network.
pct_total = mean_runtime / mean_total_runtime * 100
# Record the node's type, name of the node, mean runtime, and
# percent runtime.
node_summaries.append(
[node.op, str(node), mean_runtime, pct_total])
# One of the most important questions to answer when doing performance
# profiling is "Which op(s) took the longest?". We can make this easy
# to see by providing sorting functionality in our summary view
if should_sort:
node_summaries.sort(key=lambda s: s[2], reverse=True)
# Use the ``tabulate`` library to create a well-formatted table
# presenting our summary information
headers : List[str] = [
'Op type', 'Op', 'Average runtime (s)', 'Pct total runtime'
]
return tabulate.tabulate(node_summaries, headers=headers)
참고
We use Python’s time.time
function to pull wall clock
timestamps and compare them. This is not the most accurate
way to measure performance, and will only give us a first-
order approximation. We use this simple technique only for the
purpose of demonstration in this tutorial.
Investigating the Performance of ResNet18¶
We can now use ProfilingInterpreter
to inspect the performance
characteristics of our ResNet18 model;
interp = ProfilingInterpreter(rn18)
interp.run(input)
print(interp.summary(True))
Op type Op Average runtime (s) Pct total runtime
------------- --------------------- --------------------- -------------------
call_module maxpool 0.00724411 7.40535
call_module conv1 0.00712252 7.28105
call_module layer1_0_conv2 0.00692225 7.07632
call_module layer4_0_conv2 0.0062325 6.37122
call_module layer1_0_conv1 0.00506663 5.17941
call_module layer3_1_conv2 0.00472832 4.83356
call_module layer4_0_conv1 0.00448036 4.58009
call_module layer1_1_conv2 0.00437427 4.47163
call_module layer4_1_conv1 0.00408459 4.1755
call_module layer2_0_conv2 0.00408173 4.17258
call_module layer3_1_conv1 0.00404215 4.13212
call_module layer3_0_conv2 0.00402141 4.11091
call_module layer1_1_conv1 0.0039556 4.04365
call_module layer2_1_conv1 0.00373936 3.82259
call_module layer2_0_conv1 0.00368309 3.76507
call_module layer2_1_conv2 0.00364828 3.72948
call_module layer4_1_conv2 0.00335646 3.43116
call_module layer3_0_conv1 0.00297999 3.04632
call_module layer1_0_bn2 0.00166845 1.70559
call_module bn1 0.00154114 1.57544
call_module layer2_0_downsample_0 0.000930071 0.950772
call_module layer3_0_downsample_0 0.000650883 0.66537
call_module layer4_0_downsample_0 0.000614405 0.62808
call_module layer1_0_bn1 0.000434875 0.444555
call_module relu 0.000398159 0.407021
call_module layer1_1_bn2 0.000388145 0.396785
call_function add 0.000374794 0.383136
call_function add_1 0.000338793 0.346334
call_module fc 0.000256777 0.262492
call_module layer1_1_bn1 0.000219822 0.224715
call_module layer2_1_bn2 0.00020647 0.211066
call_function add_3 0.000201702 0.206192
call_module layer2_0_bn1 0.000190258 0.194493
call_module layer3_0_bn2 0.000177383 0.181332
call_module avgpool 0.000176907 0.180844
call_module layer4_1_bn2 0.00017333 0.177188
call_module layer2_0_bn2 0.000167608 0.171339
call_module layer2_1_bn1 0.000167608 0.171339
call_module layer3_1_bn2 0.000167608 0.171339
call_module layer3_1_bn1 0.00016284 0.166464
call_module layer1_0_relu 0.000162601 0.166221
call_module layer4_1_bn1 0.000162363 0.165977
call_module layer2_0_downsample_1 0.000154734 0.158178
call_module layer4_0_bn2 0.00015378 0.157203
call_function add_5 0.000151396 0.154766
call_module layer3_0_bn1 0.000149488 0.152816
call_module layer1_0_relu_1 0.000143528 0.146723
call_module layer1_1_relu 0.000141144 0.144285
call_module layer4_0_bn1 0.000139475 0.142579
call_module layer3_0_downsample_1 0.000130653 0.133561
call_function add_4 0.000129461 0.132343
call_module layer1_1_relu_1 0.000128984 0.131855
call_module layer4_0_downsample_1 0.000128984 0.131855
call_function add_2 0.000124693 0.127468
call_function add_7 0.000122786 0.125519
call_function add_6 0.000109434 0.11187
call_module layer2_1_relu_1 0.000108242 0.110651
call_module layer3_1_relu 0.000107527 0.10992
call_module layer2_0_relu 0.000105381 0.107727
call_module layer3_0_relu 0.000104904 0.107239
call_module layer2_1_relu 9.82285e-05 0.100415
call_module layer2_0_relu_1 9.77516e-05 0.0999274
call_module layer4_0_relu 9.60827e-05 0.0982213
call_module layer4_1_relu 9.60827e-05 0.0982213
call_module layer3_0_relu_1 9.20296e-05 0.094078
call_module layer4_0_relu_1 8.96454e-05 0.0916407
call_module layer4_1_relu_1 8.9407e-05 0.091397
call_module layer3_1_relu_1 8.86917e-05 0.0906658
call_function flatten 4.95911e-05 0.0506949
placeholder x 4.76837e-05 0.0487451
output output 3.17097e-05 0.0324155
There are two things we should call out here:
MaxPool2d
takes up the most time. This is a known issue: https://github.com/pytorch/pytorch/issues/51393BatchNorm2d also takes up significant time. We can continue this line of thinking and optimize this in the Conv-BN Fusion with FX tutorial.
Conclusion¶
As we can see, using FX we can easily capture PyTorch programs (even ones we don’t have the source code for!) in a machine-interpretable format and use that for analysis, such as the performance analysis we’ve done here. FX opens up an exciting world of possibilities for working with PyTorch programs.
Finally, since FX is still in beta, we would be happy to hear any feedback you have about using it. Please feel free to use the PyTorch Forums (https://discuss.pytorch.org/) and the issue tracker (https://github.com/pytorch/pytorch/issues) to provide any feedback you might have.
Total running time of the script: ( 0 minutes 0.452 seconds)


Welcome to gaming - without the application. GME is a discord bot that allows for 3rd party online-based and lcoally hosted tools to create a fun user experiece for all.


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How to permanently delete a site from this DNN installation using the Site Management module.
Note 1: The site set as the host site cannot be deleted. As a workaround you can set another site as the host site and then delete the site.
Note 2: Sites that belong to a Site Group cannot be deleted unless they are first removed from the Site Group.


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Package Hierarchies:
com.facebook.zookeeper
,
com.facebook.zookeeper.app
,
com.facebook.zookeeper.cmd
,
com.facebook.zookeeper.connection
,
com.facebook.zookeeper.convenience
,
com.facebook.zookeeper.election
,
com.facebook.zookeeper.mock
,
com.facebook.zookeeper.path
Class Hierarchy
java.lang.
Object
com.facebook.zookeeper.
BasicZooKeeper
(implements com.facebook.zookeeper.
ZooKeeperIface
)
com.facebook.zookeeper.
BasicZooKeeperFactory
(implements com.facebook.zookeeper.
ZooKeeperFactory
)
com.facebook.zookeeper.
DefaultZooKeeperFactory
(implements com.facebook.zookeeper.
ZooKeeperFactory
)
com.facebook.zookeeper.mock.
MockWatcher
(implements org.apache.zookeeper.Watcher)
com.facebook.zookeeper.mock.
MockZkConnectionManager
(implements com.facebook.zookeeper.connection.
ZkConnectionManager
)
com.facebook.zookeeper.mock.
MockZooKeeper
(implements com.facebook.zookeeper.
ZooKeeperIface
)
com.facebook.zookeeper.mock.
MockZooKeeperDataStore
com.facebook.zookeeper.mock.
MockZooKeeperFactory
(implements com.facebook.zookeeper.
ZooKeeperFactory
)
com.facebook.zookeeper.
RecoveringZooKeeper
(implements com.facebook.zookeeper.
ZooKeeperIface
)
com.facebook.zookeeper.
RecoveringZooKeeperFactory
(implements com.facebook.zookeeper.
ZooKeeperFactory
)
com.facebook.zookeeper.app.
ZkApplication
com.facebook.zookeeper.connection.
ZkConnectionManagerImpl
(implements com.facebook.zookeeper.connection.
ZkConnectionManager
)
com.facebook.zookeeper.path.
ZkGenericPath
com.facebook.zookeeper.election.
ZkLeaderElection
(implements com.facebook.zookeeper.election.
LeaderElection
)
com.facebook.zookeeper.path.
ZkPathCore
com.facebook.zookeeper.path.
ZkPathCoreBuilder
com.facebook.zookeeper.convenience.
ZkQuickConnection
com.facebook.zookeeper.convenience.
ZkQuickConnectionManager
com.facebook.zookeeper.convenience.
ZkScript
com.facebook.zookeeper.cmd.
ZNodeBulkLoader
com.facebook.zookeeper.cmd.
ZNodeDump
com.facebook.zookeeper.cmd.
ZNodePruner
com.facebook.zookeeper.cmd.
ZNodeSync
com.facebook.zookeeper.
ZkUtil
Interface Hierarchy
com.facebook.zookeeper.
Encodable
com.facebook.zookeeper.election.
LeaderElectionCallback
com.facebook.zookeeper.election.
LeaderElectionObserver
com.facebook.zookeeper.election.
LeaderElection
com.facebook.zookeeper.connection.
ZkConnectionManager
com.facebook.zookeeper.
ZooKeeperFactory
com.facebook.zookeeper.
ZooKeeperIface
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Accordion Demo
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Provides native R access to Interactive Brokers Trader Workstation API.
| Version: | 0.9-12 |
| Depends: | xts, zoo |
| Published: | 2014-09-22 |
| Author: | Jeffrey A. Ryan |
| Maintainer: | Joshua M. Ulrich <josh.m.ulrich at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| In views: | Finance |
| CRAN checks: | IBrokers results |
| Reference manual: | IBrokers.pdf |
| Vignettes: |
IBrokers: Interactive Brokers and R IBrokersRef Real Time Market and Trade Execution with R |
| Package source: | IBrokers_0.9-12.tar.gz |
| Windows binaries: | r-devel: IBrokers_0.9-12.zip, r-release: IBrokers_0.9-12.zip, r-oldrel: IBrokers_0.9-12.zip |
| OS X binaries: | r-release: IBrokers_0.9-12.tgz, r-oldrel: IBrokers_0.9-12.tgz |
| Old sources: | IBrokers archive |
Please use the canonical form https://CRAN.R-project.org/package=IBrokers to link to this page.


Blitzkrieg is a method of warfare where the attacker, spearheaded using a force concentration of armoured and motorised or mechanised infantry
formations with close air support, breaks through the opponent's line of defence by short, fast, powerful attacks and then dislocates the defenders,
using speed and surprise to encircle them with the help of air superiority.[1][2][3] Through the employment of combined arms in manoeuvre warfare,
blitzkrieg attempts to unbalance the enemy by making it difficult for it to respond to the continuously changing front, then defeat it in a decisive
Vernichtungsschlacht. During the interwar period, aircraft and tank technologies matured and were combined with systematic application of the traditional German tactic of
Bewegungskrieg (maneuver warfare), deep penetrations and the bypassing of enemy strong points to encircle and destroy enemy forces in a Kesselschlacht
(cauldron battle).[3][6] During the Invasion of Poland, Western journalists adopted the term blitzkrieg to describe this form of armoured warfare.[7]
The term had appeared in 1935, in a German military periodical Deutsche Wehr (German Defence), in connection to quick or lightning warfare.[8]
German manoeuvre operations were successful in the campaigns of 1939–1941 and by 1940 the term blitzkrieg was extensively used in Western media.[9][10]
Blitzkrieg operations capitalized on surprise penetrations (e.g., the penetration of the Ardennes forest region), general enemy unreadiness and their
inability to match the pace of the German attack. During the Battle of France, the French made attempts to re-form defensive lines along rivers but were
frustrated when German forces arrived first and pressed on.[10]
Despite being common in German and English-language journalism during World War II, the word Blitzkrieg was never used by the Wehrmacht as an official military term,
except for propaganda.[9] Some senior officers, including Kurt Student, Franz Halder and Johann Adolf von Kielmansegg, even disputed the idea that it was a military concept.
Kielmansegg asserted that what many regarded as blitzkrieg was nothing more than "ad hoc solutions that simply popped out of the prevailing situation".
Student described it as ideas that "naturally emerged from the existing circumstances" as a response to operational challenges.[12] The Wehrmacht never officially adopted
it as a concept or doctrine.[a]
Some elements that were crucial to the success of the tactic were:


sqrt(expr)
Takes the square root of the result of the expr. This function works on both gridded and station data. Values in expr that are less than zero are set to missing in the result.
expr


The Kernel Trick is a technique in machine learning to avoid some intensive computation in some algorithms, which makes some computation goes from infeasible to feasible.
Concretely, when you see the pattern of xiT xj ( the dot product of xi transpose and xj , where x is an observation ): xiT xj can be replaced by K(xi xj). Here, K is a kernel, which has many implementations: linear, polynomial, RBF, etc.
The most well-known case would be the Support Vector Machines. The problem state of SVM is a constrained optimization problem as follows:
To solve the above efficiently, usually the problem is modeled by as a Lagrange primal problem statement[1] (above) but solved by the dual version of problem statement, which becomes the following:
Notice the red I boxed above, that is a very computationally intensive operation. Suppose x is a (10000 x 1) vector, the dot product would introduce 10000 * 10000 = 100000000 multiplications. Suppose you use a linear kernel K(xi, xj) to replace the above red boxed part, there will be only ~10000 multiplications.
It means it is ~10000 times faster.
Machine learning, in practice, will always try to model problems into above or other ways such that the problem can be solved much more efficiently. The Kernel Trick is one of the most important one. The above is an extremely simplified version of what happened only, interested readers should dive deeper into related literature.
[1] – The primal and dual (together called the duality) are the two sides of the problem, which solving the dual problem statement gives the optimal solution to the primal problem statement (under some constraints). This is grossly simplified.


module Thread:sig
..end
Lightweight threads for Posix 1003.1c
and Win32.
type
t
The type of thread handles.
val create : ('a -> 'b) -> 'a -> t
Thread.create funct arg
creates a new thread of control,
in which the function application funct arg
is executed concurrently with the other threads of the program.
The application of Thread.create
returns the handle of the newly created thread.
The new thread terminates when the application funct arg
returns, either normally or by raising the Thread.Exit
exception
or by raising any other uncaught exception.
In the last case, the uncaught exception is printed on standard error,
but not propagated back to the parent thread. Similarly, the
result of the application funct arg
is discarded and not
directly accessible to the parent thread.
val self : unit -> t
Return the handle for the thread currently executing.
val id : t -> int
Return the identifier of the given thread. A thread identifier is an integer that identifies uniquely the thread. It can be used to build data structures indexed by threads.
exception Exit
Exception raised by user code to initiate termination of the
current thread.
In a thread created by Thread.create
funct
arg
, if the
Thread.Exit
exception reaches the top of the application
funct arg
, it has the effect of terminating the current thread
silently. In other contexts, there is no implicit handling of the
Thread.Exit
exception.
val exit : unit -> unit
Raise the Thread.Exit
exception.
In a thread created by Thread.create
, this will cause the thread
to terminate prematurely, unless the thread function handles the
exception itself. Fun.protect
finalizers and catch-all
exception handlers will be executed.
To make it clear that an exception is raised and will trigger
finalizers and catch-all exception handlers, it is recommended
to write raise Thread.Exit
instead of Thread.exit ()
.
Thread.create
.val delay : float -> unit
delay d
suspends the execution of the calling thread for
d
seconds. The other program threads continue to run during
this time.
val join : t -> unit
join th
suspends the execution of the calling thread
until the thread th
has terminated.
val yield : unit -> unit
Re-schedule the calling thread without suspending it. This function can be used to give scheduling hints, telling the scheduler that now is a good time to switch to other threads.
The functions below are leftovers from an earlier, VM-based threading
system. The Unix
module provides equivalent functionality, in
a more general and more standard-conformant manner. It is recommended
to use Unix
functions directly.
val wait_timed_read : Unix.file_descr -> float -> bool
val wait_timed_write : Unix.file_descr -> float -> bool
Suspend the execution of the calling thread until at least
one character or EOF is available for reading (wait_timed_read
) or
one character can be written without blocking (wait_timed_write
)
on the given Unix file descriptor. Wait for at most
the amount of time given as second argument (in seconds).
Return true
if the file descriptor is ready for input/output
and false
if the timeout expired.
The same functionality can be achieved with Unix.select
.
val select : Unix.file_descr list ->
Unix.file_descr list ->
Unix.file_descr list ->
float -> Unix.file_descr list * Unix.file_descr list * Unix.file_descr list
Same function as Unix.select
.
Suspend the execution of the calling thread until input/output
becomes possible on the given Unix file descriptors.
The arguments and results have the same meaning as for
Unix.select
.
val wait_pid : int -> int * Unix.process_status
Same function as Unix.waitpid
.
wait_pid p
suspends the execution of the calling thread
until the process specified by the process identifier p
terminates. Returns the pid of the child caught and
its termination status, as per Unix.wait
.
Signal handling follows the POSIX thread model: signals generated
by a thread are delivered to that thread; signals generated externally
are delivered to one of the threads that does not block it.
Each thread possesses a set of blocked signals, which can be modified
using Thread.sigmask
. This set is inherited at thread creation time.
Per-thread signal masks are supported only by the system thread library
under Unix, but not under Win32, nor by the VM thread library.
val sigmask : Unix.sigprocmask_command -> int list -> int list
sigmask cmd sigs
changes the set of blocked signals for the
calling thread.
If cmd
is SIG_SETMASK
, blocked signals are set to those in
the list sigs
.
If cmd
is SIG_BLOCK
, the signals in sigs
are added to
the set of blocked signals.
If cmd
is SIG_UNBLOCK
, the signals in sigs
are removed
from the set of blocked signals.
sigmask
returns the set of previously blocked signals for the thread.
val wait_signal : int list -> int
wait_signal sigs
suspends the execution of the calling thread
until the process receives one of the signals specified in the
list sigs
. It then returns the number of the signal received.
Signal handlers attached to the signals in sigs
will not
be invoked. The signals sigs
are expected to be blocked before
calling wait_signal
.
val default_uncaught_exception_handler : exn -> unit
Thread.default_uncaught_exception_handler
will print the thread's id,
exception and backtrace (if available).
val set_uncaught_exception_handler : (exn -> unit) -> unit
Thread.set_uncaught_exception_handler fn
registers fn
as the handler
for uncaught exceptions.
If the newly set uncaught exception handler raise an exception,
Thread.default_uncaught_exception_handler
will be called.


the and for []. with SARS-CoV- (). The that are patients clinical from â€" coronavirus has have was viral COVID- respiratory severe study this were treatment potential infection been protein novel SARS-CoV against being also other virus {background pneumonia reported group its acute trials HCQ cases shown This may research vaccine antiviral drug host should studies used [,]. days disease given margin received such use based drugs including these {border-style after development human showed twice .collab: SARS agents conducted not px;} which #ffffff;} ChiCTR MERS-CoV ahead care cell day first symptoms will (Epub ACE LPV/RTV RNA print). spike trial associated control infection. outbreak their therapies therapy using Coronavirus could investigated prevention protease several still two Chinese SARS-CoV-, binding can combination compared domain immune management more new significant therapeutic tocilizumab viruses Another China Clinical FPV However, RDV currently different efficacy results review safety Clinicaltrials.Gov. Infect. another detection epidemic gene health patient public reports syndrome Disease Hospital January Wuhan among case confirmed current daily diagnostic genome include infected infections most oxygen promising proteins test time well CoV NCT These Tocilizumab Wuhan, al. available dose effects global investigating over patients. plus repurposing standard structural suggested than while China. Med. SARS-CoV-. [], agent antibodies antibody approved around assay between clearance considered diagnosis diseases effect enzyme found hospitalized into lung outcomes possible px; real-time receptor six spread {font-family â€"Â -nCoV Antiviral CoVs December For Lancet(), Med.(), Novel People's RBV RT-PCR Treatment according activity alone analysis anti-inflammatory but cells conditions corticosteroids critical determine developing due effectiveness font-size further hospital https://clinicaltrials.gov/ct/show/NCT identified improvement included inhibit interaction methylprednisolone needed pandemic previously rapid recent response role same screening significantly signs silico some study. tests there through trial. under very vitro when â€¢ & (COVID-) (COVID-). About April Clin. Control Dis.(), East IL- PCR SARS-COV- USA University Virol.(), WHO all animal approach benefits candidates chloroquine combined compounds demonstrated dosing double; during each entry every evidence example, genetic glycoprotein inhibitors ivermectin kit mentioned molecular only patients' polymerase positive previous process proposed protocol provided recently review, structure studies. supportive system treat Bevacizumab CLpro ChiCTR, Clinicaltrials.gov Figure Hong March Middle NCT, Patients Research Several TCMs With addition administrated al.A better bevacizumab border-color border-width both candidate common coronaviruses days. developed direct disease. effective essential extreme fever highly hydroxychloroquine identical identify illness includes infections. infectious inflammatory intravenous investigate isolation known latest less membrane nucleic number one phase phosphate possibly procedure product production recombinant recommended registered replication resulted saturation second sequence similar solid; specific suspected they usually vaccines validate yet {font-style {font-weight (COVID-): (HCQ Accordingly, Agents Am. Besides, Biol. CDC COVID-. China, Chloroquine Currently, Emerg. Engl. February Few Furthermore, Health Kong LPV MERS Medicine Moreover, National RNA-dependent Res.(), Res., SAR-CoV- Samples Science(), Such TCM Table There Thus, Trial Zhejiang about above, acid active administration adverse al., analog antibiotics apparent asymptomatic bats blocks bold broad cellular cleavage close complications contact contribute coronavirus. courier; cytokine diagnosed distress early efficiency em; established evaluated existing failure fast following high human-to-human identification imaging immunogenicity immunomodulators improve indicates individuals infection, infections, inhibition inhibitor inhibitory involved kidney kits level limited liver lower main many measures medicine melatonin mg, mg/kg might mild moderate monoclonal natural need nitazoxanide one-step ongoing option oral others outbreak, outbreak. outcomes. people pharmaceutical point preventive primarily produce pt;} randomized range reaction receptor-binding receptors receptors. reduce regimen related retrospective risk risks sepsis since single social storm strain studied study, survival syndrome. tablets then therapeutics therapy, those throughout tool total transmission treating treatment. vaccinations versus virus. what where work Î±-ketoamide â€":


RNAfiner: Ribowave
RiboWave analyses Ribosome profiling data (Ribo-seq). It utilizes wavelet transform to denoise the original signal by extracting 3-nt periodicity of ribosomes (i.e. signal frequency) and precisely locate their footprint.
RNAtarget: RBPgroup
RBPgroup is a soft-clustering method on various CLIP-seq datasets, in order to group together RBPs that specifically bind the same RNA sites. Our approach links proteins and RNA motifs known to possess similar biochemical and cellular properties and can, when used in conjunction with additional experimental data, identify high- confidence RBP groups and their associated RNA regulatory elements.
RNAfinder: COME
COME is a tool to calculate COding potential from Multiple fEatures for a given transcript. The models in COME were trained on mRNAs and long ncRNAs (lncRNAs).
RNAtarget: POSTAR
POSTAR (CLIPdb 2) is a platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins. It enables the experimental biologists to connect protein-RNA interactions with multi-layer information of post-transcriptional regulation and functional genes, and helps them generate novel hypotheses about the post-regulatory mechanisms of phenotypes and diseases.
RNAmed: CCG
CCG, Catalogue of Cancer Genes/lncRNAs, is an assembly resource of coding and noncoding genes associated with cancer. In addition, drug-gene information in CCG provides a useful guide to the development of new anti-cancer drugs.
RNAstructurome: RNAex
RNAex is an RNA secondary structure prediction server enhanced by high-throughput experimental data. We have re-mapped raw data of the published probing experiments to the whole genome, thus users can predict secondary structures for novel RNA transcripts.
RNAfinder: RNAfeature
RNAfeature provides a common set of conserved features for ncRNAs across multiple species. The models in RNAfeature were trained on canonical ncRNAs (e.g.,tRNAs,rRNAs, miRNAs, snRNAs, snoRNAs, 7SK RNAs, Y RNAs).
RNAfinder Cloud Server (beta)
An app to for processing RNA-seq data, identify and characterize known and novel transcripts, including protein coding genes and noncoding RNAs.
RNAstructurome: RME
RME is a tool for RNA secondary structure prediction with multiple types of experimental probing data. It is based on the RNAstructure package. It also provides preprocessing scripts for transforming the SHAPE, PARS and DMS-seq data into pairing probability according a posterior probabilistic model. Moreover, it also contains a utility for optimizing the parameters of RME by RME-Optimize.
RNAtarget: CLIPdb
CLIPdb is an integrative resource of CLIP-seq studies. It aims to characterize the regulatory networks between RNA binding proteins (RBPs) and various RNA transcript classes by integrating large amounts of CLIP-seq (including HITS-CLIP, PAR-CLIP and iCLIP as variantions) data sets.


--- layout: post title: Tomato description: "Time efficiency" modified: 2015-04-16 tags: [] image: feature: cherry-tomato.jpg comments: false ---
I have been thinking to try Pomodoro technique for a while now, but it can be hard to justify taking 5 min break every 25 minutes. In case you are not familiar with the technique, the premise is that our brains can't focus effectively on something for longer than 25 minutes, at which point it needs a break or distraction of some sort. Getting up to move around and stretch your muscles somewhat, is a good use of the break time, anything, really, that would shift focus away from the task at hand.
My final project at the bootcamp was based around the technique, it was a task tracker with Pomodoro integrated. These days every time I think about this project, all I think of is how many crimes against JS and good code design in general I have comitted at a time.
So, anyway, enough reminiscing, long story short, we tried it this week! I say we is because I spent a week pairing with Sam, a fellow resident, on Mastermind game. And he has been trying to use it, so it was a good opportunity to do it together, obviously. We also got other residents in on it, which made things even better.
We tried to use the break time wisely, we would practice juggling or type racing. Doing it together as a team made a whole lot easier to stick to it, as it can be hard to pull away from the task when the timer goes off. When you are working by yourself then it is much easier to just ignore the reminder to take a break and to just carry on working because you feel that you are really focusing on a task.
So far I would say that it helped us stay more alert in the afternoon, I didn't feel the slump in energy and productivity that you often can feel in the afternoon. Right now the plan is to stick to the technique and see how it goes, but so far we see its positive effect, and I will report back on this after we have used it for sometime.
PS Pomodoro stands for tomato in Italian, hence the title, hope it wasn't too cryptic :)


MAX LAX LEMON- magnesium citrate liquid
Command Brands, LLC
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
• relieves occasional constipation (irregularity) • generally produces bowel movement in 1/2 to 6 hours
Ask a doctor before use if you have • kidney disease • a magnesium restricted diet • a sodium restricted diet • stomach pain, nausea or vomiting • noticed a sudden change in bowel habits that last more than 1 week
• you have rectal bleeding or no bowel movement after use.
These could be a sign of a serious condition. • you need to use a laxative more than one week.
In case of overdose, get medical help or contact a Poison Control Center right away.
adults and children 12 years 1/2 to 1 bottle (10 fl. oz.). Drink a full 8 oz. glass of liquid with each dose
and older The dose may be taken as a single daily dosde or in divided doses
children 6 to11 years of age 1/3 to 1/2 with a full 8 oz. glass of liquid
children under 6 years of age consult a doctor
|
MAX LAX
LEMON magnesium citrate liquid | ||||||||||||||||||
| ||||||||||||||||||
| ||||||||||||||||||
| ||||||||||||||||||
| ||||||||||||||||||
| ||||||||||||||||||
|
| Labeler - Command Brands, LLC (060684113) |
| Establishment | |||
| Name | Address | ID/FEI | Business Operations |
|---|---|---|---|
| Aaron Industries, Inc. | 101896231 | manufacture(69197-002) |


--- layout: post title: Mild refresh of rubyonrails.org categories: [] author: David published: true date: 2008-12-21 18:25:00.000000000 +00:00 ---
We’ve just launched a mild refresh of the rubyonrails.org site. It now runs on Radiant through Passenger at Slicehost and includes a nip and tuck in various places along with a couple of new pages. Read about how we prefer to deploy Rails applications, the ecosystem that has sprung up around Rails, and see the much expanded list of prolific Rails applications.


| lavListInspect {lavaan} | R Documentation |
The lavListInspect()
and lavListTech()
functions can be used to
inspect/extract information that is stored inside (or can be computed from) a
lavaanList object.
lavListInspect(object, what = "free", add.labels = TRUE, add.class = TRUE, list.by.group = TRUE, drop.list.single.group = TRUE) lavListTech(object, what = "free", add.labels = FALSE, add.class = FALSE, list.by.group = FALSE, drop.list.single.group = FALSE)
object |
An object of class |
what |
Character. What needs to be inspected/extracted? See Details for a
full list. Note: the |
add.labels |
If |
add.class |
If |
list.by.group |
Logical. Only used when the output are model matrices.
If |
drop.list.single.group |
If |
The lavListInspect()
and lavListTech()
functions only differ in
the way they return the results. The lavListInspect()
function will
prettify the output by default, while the lavListTech()
will not attempt
to prettify the output by default.
Below is a list of possible values for the what
argument, organized
in several sections:
Model matrices:
"free"
:A list of model matrices. The non-zero integers
represent the free parameters. The numbers themselves correspond
to the position of the free parameter in the parameter vector.
This determines the order of the model parameters in the output
of for example coef()
and vcov()
.
"partable"
:A list of model matrices. The non-zero integers
represent both the fixed parameters (for example, factor loadings
fixed at 1.0), and the free parameters if we ignore any equality
constraints. They correspond with all entries (fixed or free)
in the parameter table. See parTable
.
"start"
:A list of model matrices. The values represent
the starting values for all model parameters.
Alias: "starting.values"
.
Information about the data (including missing patterns):
"group"
:A character string. The group variable in the data.frame (if any).
"ngroups"
:Integer. The number of groups.
"group.label"
:A character vector. The group labels.
"level.label"
:A character vector. The level labels.
"cluster"
:A character vector. The cluster variable(s) in the data.frame (if any).
"nlevels"
:Integer. The number of levels.
"ordered"
:A character vector. The ordered variables.
"nobs"
:Integer vector. The number of observations in each group that were used in the analysis (in each dataset).
"norig"
:Integer vector. The original number of observations in each group (in each dataset).
"ntotal"
:Integer. The total number of observations that
were used in the analysis. If there is just a single group, this
is the same as the "nobs"
option; if there are multiple groups,
this is the sum of the "nobs"
numbers for each group
(in each dataset).
Model features:
"meanstructure"
:Logical. TRUE
if a meanstructure
was included in the model.
"categorical"
:Logical. TRUE
if categorical endogenous
variables were part of the model.
"fixed.x"
:Logical. TRUE
if the exogenous x-covariates
are treated as fixed.
"parameterization"
:Character. Either "delta"
or
"theta"
.
"list"
:The parameter table. The same output as given
by parTable()
.
"options"
:List. The option list.
"call"
:List. The call as returned by match.call, coerced to a list.
# fit model HS.model <- ' visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9 ' # a data generating function generateData <- function() simulateData(HS.model, sample.nobs = 100) set.seed(1234) fit <- semList(HS.model, dataFunction = generateData, ndat = 5, store.slots = "partable") # extract information lavListInspect(fit, "free") lavListTech(fit, "free")


 Take control of me through three levels to discover my latest announcement.
- Use the left and right arrow key to move in either direction.
- Hold the down arrow key to fall through a platform or slope.
- Press space to jump.
- Collect all the coins in each level to hear more of the story.
You will need to play this on a PC, not phone or tablet - sorry!


MAJOR ACADEMIC PROJECTS
CS 16: Thesis 1 (2nd Semester AY 2021-2022)
"WebAutomata: Web based game in learning Automata"
- Computer-aided technologies serve as the basis of organizational tools
used to create complex logical systems using mathematics.
CS 11/L: Software Engineering 1 (2nd Semester AY 2021-2022)
"QR code Contact Tracing traveling to Roxas from Puerto Princesa City and Vice versa"
- QR code are given to passengers with their information for easy and safe transactions.
OTHER RELEVANT INFORMATION
- Call of Duty Mobile Champion Team RV, MNL Tournament
- Ranked as Eagle Scout in Boy Scout of the Philippines
- Attended numerous seminars and webinars in the field of Computer Science
- Leadership award in Boy Scout in the Philippines


#include <scheduler.h>
Inheritance diagram for Scheduler:
Public Member Functions | |
| Scheduler () | |
| virtual | ~Scheduler () |
| virtual void | ReadyToRun (Thread *thread) |
| virtual Thread * | FindNextToRun () |
| void | SwitchTo (Thread *nextThread) |
| void | Print () |
Protected Attributes | |
| Listint * | readyList |
|
|
Constructor. Initialize list of ready threads. Constructor. Initialize the list of ready but not running threads to empty. |
|
|
Destructor. De-allocate the ready list. Destructor. De-allocate the list of ready threads. |
|
|
Dequeue first thread of the ready list, if any, and return thread. Return the next thread to be scheduled onto the CPU. If there are no ready threads, return NULL. Side effect: Thread is removed from the ready list.
Reimplemented in FixedPriorityScheduler. |
|
|
Print contents of ready list. Print the scheduler state -- in other words, the contents of the ready list. For debugging. |
|
|
Inserts a thread in the ready list. Mark a thread as ready, but not running. Put it on the ready list, for later scheduling onto the CPU.
Reimplemented in FixedPriorityScheduler. |
|
|
Cause context switch to nextThread. Dispatch the CPU to nextThread. Save the state of the old thread, and load the state of the new thread, by calling the machine dependent context switch routine, SWITCH. Note: we assume the state of the previously running thread has already been changed from running to blocked or ready (depending). Side effect: The global variable currentThread becomes nextThread.
|
|
|
Queue of threads that are ready to run,but not running.
|


EYAKUZE: Vote (or don’t), but leave Church out of it
by ELSIE EYAKUZEIn Summary
- If folks need to go to the polls, let them do so in good faith and not as blind followers of an institution that should always beware of its immense power and how it uses it.
Uh oh. You see, I totally believe in the separation of Church and State. As a lapsed Catholic, I especially believe in the separation of church and state, except when I don’t because all humans are a little bit contradictory.
Let me tell you the story of the Holy Roman Catholic Church and its influence on politics. See, once upon a time...Just kidding!
Context: so basically Tanzanians are saying they don’t really feel like voting in the local government elections because their teams are not playing, and where is the fun in that?
The coalition of major opposition parties has taken its ball and huffed off the field because the referee and the linesmen are all bought out so what is the point. Some people approve of this, some do not.
A Bishop of our Catholic Church has decided to tell people not to avoid voting. No problem: They are entitled to an opinion just like any other organisation except you know that bit where I was going to tell you about the history of the Holy Roman Catholic Church? Yeah. They’re not just any organisation. Two thousand years of history there. Two thousand years.
Just a few years back, major faith organisations came together to write open letters to the government to be more...humane, peace-loving and kind. All the good spiritual kind of advocacy. That is why I am only against the separation of Church and State when it suits me.
Related Stories
-
EYAKUZE: Poll fuss will pass and we’ll have our holiday
-
ULIMWENGU: Our civic elections are a no-brainer, but expect DJ Khaled to keep rapping
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But here we are now, a lot has changed. In the time the Church has existed, it has done everything imaginable under the sun. It has been on both sides of history, has supported the meek and the mild and also supported the oppressors of the poor and downtrodden—sometimes all at once.
But we have the benefit of hindsight. And like any learning institution, even the Church has had to give in to the idea of progress.
Funnily enough, as it progresses, the Church is moving ever so slightly closer back to the teachings of a somewhat rebellious Jewish prophet—with what looks suspiciously like socialist ideals.
The current Pope is really trying to work for a more inviting and accepting Church, which is not easy at all. I think the underlying problem is and always has been diversity.
Across the millions and millions of believers around the world, how can we really make one size fit all? Faith is important to us as Africans and I always fear when it is used directly to endorse any regime. Bad things can ensue.
It seems strange to protest a Bishop’s directive on whether or not the faithful should vote while simultaneously making the case that they should always advocate on behalf of the people for restraint and peacefulness from their governments.
But can you call yourself a properly lapsed Catholic if you don’t embrace contradiction? That would be odd.
If folks need to go to the polls, let them do so in good faith and not as blind followers of an institution that should always beware of its immense power and how it uses it.
Elsie Eyakuze is a consultant and blogger for The Mikocheni Report. E-mail: elsieeyakuze@gmail.com


This is a follow up to a previous post: "The MEAN Stack on Heroku", which looked at deploying and building a MEAN stack application in the cloud via Heroku.
This time, I'm doing the exact same thing on IBM Bluemix, but this time I will focus primarily on configuring the Bluemix MongoDB service, as the rest is the same as when using Heroku.
You first need to install the MongoLab service on Bluemix and register it to your app. You can do that using cloud foundry from the command line, or just add it from the web interface.
Once MongoLab is installed and connected to your app, you’ll need to ensure your mongoose connection is set to use Bluemix's environmental variable, as well as your own (so you don't break how things work locally). That can be done by altering the way you connect to the database usingmongoose.connect()
. For my app, I altered my db.js file to following:
if (process.env.VCAP_SERVICES) { var env = JSON.parse(process.env.VCAP_SERVICES); for (var svcName in env) { if (svcName.match(/^mongo.*/)) { mongoose.connect(env['mongolab'][0].credentials.uri); console.log ("Connected to mongodb service"); } } } else{ mongoose.connect('mongodb://localhost/social'); }
Bluemix stores the mongodb connection variable in an environmental variable called VCAP_SERVICES
, so we need to find it and use it within mongoose.connect()
. Here's what's happening in the above code snippet:
VCAP_SERVICES
variable into a local variable called env Apart from doing this there's one more bit that needs fixing before the app will run:
When creating a node server locally, it's pretty standard to use port 3000. However, I found Bluemix doesn't like this, as it also has its own environmental variable for the port too: process.env.VCAP_APP_PORT, which you can use like so:
var port = 3000; if (process.env.VCAP_APP_PORT !== null) { port = process.env.VCAP_APP_PORT; } app.listen(port, function(){ console.log('listening on ', 3000) })


E-cell
ABOUT THE EVENT
1.Adhole:
- Participants will make a team of two people .
- They will be given a product(name) and have to write an advertisement based on the given product .
- Judgement will be on basis of content.
2. Trading Strategies:
- Trading Strategies includes tactics of buying and selling the prouduct after the product have been launched.
- Participants will make a team of two people .
- And it will have three rounds-
- Round1: Quiz
- Quiz will be based on general business and management .
Selected team after this round will go for next two rounds.
- Round2:Product Launch
- Each team will be provided with any random product and they have to launch the product.
- Judgement will be on the basis how they present their ideas and motives behind launching the product.
- Round3: Product sell/Best seller
- Each team have to sell the product that they have launched in round 2.
- They have to put their ideas regarding the quality ,price,features of the product. They have to sell the product in convenient way.
3. Best Manager:
- Individual participant will be provided with a problem statement related to some particular situation that may arise in an organisation and they to give solution for the problem and how they handle and proceed in that situation.
Head Co-ordinators
Co-ordinators
- Beauty Gupta
- Gaurav Singh


Another definition of addition
Give a definition of addition on nat, recursive on its
second argument (and not the first one, as plus).
Prove that your functions computes the same result as Coq's plus.
Solution
Look at this file
Going home
Pierre Castéran


Now pleasing sleep had seal’d each mortal eye,
Stretch’d in the tents the Grecian leaders lie:
The immortals slumber’d on their thrones above;
All, but the ever-wakeful eyes of Jove.(76)
To honour Thetis’ son he bends his care,
And plunge the Greeks in all the woes of war:
Then bids an empty phantom rise to sight,
And thus commands the vision of the night.
“Fly hence, deluding Dream! and light as air,(77)
To Agamemnon’s ample tent repair.
Bid him in arms draw forth the embattled train,
Lead all his Grecians to the dusty plain.
Declare, e’en now ‘tis given him to destroy
The lofty towers of wide-extended Troy.
For now no more the gods with fate contend,
At Juno’s suit the heavenly factions end.
Destruction hangs o’er yon devoted wall,
And nodding Ilion waits the impending fall.”
Swift as the word the vain illusion fled,
Descends, and hovers o’er Atrides’ head;
Clothed in the figure of the Pylian sage,
Renown’d for wisdom, and revered for age:
Around his temples spreads his golden wing,
And thus the flattering dream deceives the king.


This example evaluates a complex function at eight locations encircling a specified point of the complex plane. By decreasing the radius of the circle of evaluation, one can get an idea of the nature of the function at that complex point:
The point location is entered as a string of the sort one normally uses to express complex numbers. The function to be inspected must use x
as the independent variable.
Complete code for this example:


Game Design Adventures
The Sea | Solo project
You | Solo project
Maestro | HTC Vive
Isetta Game Engine
Althea | Co-op VR
Prism | Autism Awareness
Eat My Carrot! | GGJ 2018
Ripple Blossom | Indie game
You're Fired | Omni + Oculus
The Sound Bearier | HoloLens
A Full-time Job...


Research Methods for Global Studies
Class 3: Hypothesis Testing and Relationships Between Variables
Abelardo Gómez Díaz
Maximum Likelihood Estimators
Estimate that is most consistent with the data -- that maximizes the likelihood
Hypothesis
- A statement about a population -- usually about the value of a parameter or parameters
- Usually phrased as null hypothesis ($H_0$) vs. alternative hypothesis ($H_a$)
Significance Test
- Evaluates hypothesis based on evidence from a sample
- Usually focuses on whether sampling variation can be ruled out as cause of observed differences
- 5 parts:
- Assumptions
- Hypothesis
- Test Statistic
- p-value
- Conclusion
Assumptions
- Type of data
- Randomization
- Population distribution
- Sample size
Test statistic
Summarizes how far estimate falls from parameter value in $H_0$.
p-value
If $H_0$ were true, what is the probability that the test statistic would be at least as extreme as the observed value?
Conclusion
- p-value
- either reject $H_0$ and accept $H_a$ or fail to reject $H_0$
Example: Student Height
We have been told that the average height among students is 70 inches. We draw a sample of 100 students and measure their height. The mean is 69.8 inches with a sample standard deviation of 1.
Example: Student Height
Assumptions
- random sample
- approximately normal population distribution
Example: Student Height
Hypothesis
- $H_0$: population mean is 70 ($\mu=70$)
- $H_a$: population mean is not 70 ($\mu\neq70$)
Example: Student Height
Test statistic
$$t = \frac{\bar{y} - \mu_0}{se} = \frac{69.8 - 70}{0.1} = -2$$
where
$$se = \frac{s}{\sqrt{n}} = \frac{1}{\sqrt{100}} = .1$$
Example: Student Height
p-value
Example: Student Height
p-value
Example: Student Height
p-value
> 2*(pt(-2, df=99))
[1] 0.04823969
Example: Student Height
Conclusion
$$p=.048$$
so what?
Choosing $\alpha$-level
- Type I error: rejecting $H_0$ when it is true.
- Type II error: failing to reject $H_0$ when it is false.
Relationships Between Variables
Is Height Related to Shoe Size
Is Height Related to Shoe Size
Prediction Equation
$$\hat{y} = a + bx$$
Ordinary Least Squares Calculations
$$b = \frac{\sum\left(x - \bar{x}\right)\left(y - \bar{y}\right)}{\sum\left(x-\bar{x}\right)^2}$$
$$a = \bar{y} - b\bar{x}$$
Correlation
- a standardized version of the slope in the regression equation
$$r = \left(\frac{s_x}{s_y}\right)b$$
Multiple variables
$$\hat{y} = \alpha + \beta_1x_1 + \beta_2x_2$$
Multiple variables
|
| Dependent variable: |
| change |
| setting | 0.271** |
| (0.108) |
| effort | 0.968*** |
| (0.225) |
| Constant | -14.451* |
| (7.094) |
| Observations | 20 |
| Adjusted R2 | 0.707 |
| Note: | *p<0.1; **p<0.05; ***p<0.01 |
Visualizing in 3-Dimensional Space
3D
Categorical Independent Variables
$$\hat{y} = \alpha + \beta_1{x_1} + \beta_2{\textrm{D}}$$
Categorical Independent Variables
|
| Dependent variable: |
| change |
| setting | 0.169 |
| (0.106) |
| effort.Moderate | 4.144 |
| (3.191) |
| effort.Strong | 19.448*** |
| (3.729) |
| Constant | -5.954 |
| (7.166) |
| Observations | 20 |
| Adjusted R2 | 0.764 |
| Note: | *p<0.1; **p<0.05; ***p<0.01 |
Visualizing Categorical Differences
Visualizing the Model
Interaction Terms
$$\hat{y} = \alpha + \beta_1x_1 + \beta_2\textrm{D} + \beta_3x_1\textrm{D}$$
Interaction Terms
|
| Dependent variable: |
| change |
| setting | 0.184 |
| (0.140) |
| effortModerate | 9.845 |
| (15.575) |
| effortStrong | -19.595 |
| (50.770) |
| setting:effortModerate | -0.087 |
| (0.233) |
| setting:effortStrong | 0.457 |
| (0.604) |
| Constant | -6.880 |
| (9.317) |
| Observations | 20 |
| Adjusted R2 | 0.745 |
| Note: | *p<0.1; **p<0.05; ***p<0.01 |
Visualizing the Model
Interacting Continuous Variables
$$\hat{y} = \alpha + \beta_1x_1 + \beta_2x_2 + \beta_3x_1x_2$$
Interacting Continuous Variables
|
| Dependent variable: |
| change |
| setting | 0.189 |
| (0.162) |
| effort | -0.039 |
| (1.473) |
| setting:effort | 0.013 |
| (0.019) |
| Constant | -8.502 |
| (11.218) |
| Observations | 20 |
| Adjusted R2 | 0.698 |
| Note: | *p<0.1; **p<0.05; ***p<0.01 |
Visualizing in 3-Dimensional Space
3D
Beyond Straight Lines
$$\hat{y} = \alpha + \beta_1x_1 + \beta_2x_1^2$$
Beyond Straight Lines
|
| Dependent variable: |
| change |
| setting | -0.690 |
| (1.064) |
| setting_sq | 0.009 |
| (0.008) |
| Constant | 14.721 |
| (33.925) |
| Observations | 20 |
| R2 | 0.492 |
| Adjusted R2 | 0.432 |
| Residual Std. Error | 8.903 (df = 17) |
| Note: | *p<0.1; **p<0.05; ***p<0.01 |
Beyond Straight Lines
Comparing Models
|
| Dependent variable: |
| change |
| (1) | (2) |
| setting | 0.505*** | -0.690 |
| (0.131) | (1.064) |
| setting_sq | | 0.009 |
| | (0.008) |
| Constant | -22.125** | 14.721 |
| (9.642) | (33.925) |
| Observations | 20 | 20 |
| Adjusted R2 | 0.423 | 0.432 |
| Residual Std. Error | 8.973 (df = 18) | 8.903 (df = 17) |
| Note: | *p<0.1; **p<0.05; ***p<0.01 |
Binary Dependent Variables
- Logit regression models log odds of outcome
- Results can be interpreted in terms of probability


The PS4 is Sony's most attractively designed piece of hardware. It's a beautiful system, with a sharp, slightly angled profile accented by a light bar that acts as a console status indicator.
Xbox One S seems even more significant. Much smaller, it’s still found space for an internal PSU and can stand fully vertical as well. Microsoft was also smart enough to jettison the dust-magnet glossy areas and over-sensitive power button, which came on at the slightest brush. Read more at
PC gaming is miles more accessible than it was in the past. There are practically infinite resources on the Internet for any question you might encounter, for any error code a game might spit back at you. Driver updates are done with the push of a button now and take far less time than any console firmware update.


vStudy
Courses
Subjects
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Repository
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What is this?
This page lets you manage registrations for websites using the experimental web API Ballista.
If your browser natively supports Ballista, you manage these registrations in the browser's settings panel. This page is for managing registrations made through the experimental polyfill.
Registered handlers
| Name | URL | Accepted verbs |
|---|
Delete selected handlers
Add a new handler
Enter verbs as a comma-separated list.
Add new handler.


Customization
Please Note: Styling is similar to customization but is in its own section for clarity.
Griddle comes with a number of customization options to help it fit with your project goals.
Custom Columns
Custom column components are defined in the Column Metadata object. The components are passed data and rowData properties.
- data
- object - the data that would normally be rendered in the column.
- rowData
- object - the data for all items in the same row
Example:
We are going to make the body of one of the columns a link. This link will use data from another column to determine the href.
Assume we have the following data for our grid:
var someData = [
{
"id": 0,
"name": "Mayer Leonard",
"city": "Kapowsin",
"state": "Hawaii",
"country": "United Kingdom",
"company": "Ovolo",
"favoriteNumber": 7
},
{
"id": 1,
"name": "Koch Becker",
"city": "Johnsonburg",
"state": "New Jersey",
"country": "Madagascar",
"company": "Eventage",
"favoriteNumber": 2
},
...
];
We want the name column to be a link to /speakers/state/name
(where state and name are pulled in from the data). We can define a customComponent to be rendered as follows:
var LinkComponent = React.createClass({
render: function(){
url ="speakers/" + this.props.rowData.state + "/" + this.props.data;
return {this.props.data}
}
});
From there, we will set the customComponent value in the name columnMetadata object to this LinkComponent.
var columnMeta = [
{
...
"columnName": "name",
"order": 1,
"locked": false,
"visible": true,
"customComponent": LinkComponent
},
...
];
Now, when Griddle is rendered with this columnMetadata, it should write the link as expected.
React.render(<Griddle data={someData} columnMetdata={columnMeta} />,
document.getElementById('something'));
Custom Row Format
Sometimes you may want to display grid data in a format other than a grid but still have pagination, filtering, etc. This type of formatting can be accomplished with the custom row format properties. To use custom row formatting, the useCustomRowFormat and the customRowFormat properties will need to be set.
- useCustomRowFormat
- bool - determines if custom row formats are applied
- customRowFormat
- Component - the component to render in place of a grid row. This component receives a property named data
- customRowFormatClassName
- string - the CSS class name to apply to the format component.
- allowToggleCustom
- bool - whether or not the user should be able to toggle between custom format and grid format.
Example:
We are going to render our grid as a series of cards, keeping the pagination and filtering from Griddle in tact. Assume we are using the same data in the custom column example. We will need to create a custom component as follows:
var OtherComponent = React.createClass({
getDefaultProps: function(){
return { "data": {} };
},
render: function(){
return (

{this.props.data.name}{this.props.data.company}

{this.props.data.city}

{this.props.data.state}, {this.props.data.country}

);
}
});
From there, Griddle can be rendered with the useCustomRowFormat and customRowFormat properties:
Custom Grid Format
In some cases, it may be ideal to use Griddle but display a global format other than a grid or series of rows. Assume we have an object containing temperature data for years and we want to display this data with a trend line chart. This is possible with a custom grid format component. To use custom grid formatting the useCustomGridFormat and customGridFormat properties need to be set.
- useCustomGridFormat
- bool - determines if custom row formats are applied
- customGridFormat
- Component - the component to render in place of a grid row. This component receives a property named data
- customGridFormatClassName
- string - the CSS class name to apply to the format component.
- allowToggleCustom
- bool - whether or not the user should be able to toggle between custom format and grid format.
Example:
As stated above we are going to render a visualization of temperature data rather than a chart. To start off we need to create a visualization component that uses a data property to obtain its values (the following example uses the awesome chartist library and accompanying react component):
var TestLineChart = React.createClass({
render: function(){
var simpleLineChartData = {
labels: _.keys(this.props.data[0]),
series: []
};
_.each(this.props.data, function(item){
simpleLineChartData.series.push(_.values(item));
});
return <ChartistGraph data={simpleLineChartData} type={'Line'} />
}
});
Custom Paging Component
If you want to customize the paging component, just set the property 'useCustomPager' to true and pass in another component as property named 'customPager'. The example component below shows 11 buttons (5 previous, current, 5 next):
- useCustomPager
- bool - Use custom pagination component rather than default pager. default: false
- customPager
- object - The custom pagination component. default: {}
Example
var OtherPager = React.createClass({
getDefaultProps: function(){
return{
"maxPage": 0,
"nextText": "",
"previousText": "",
"currentPage": 0,
}
},
pageChange: function(event){
this.props.setPage(parseInt(event.target.getAttribute("data-value")));
},
render: function(){
var previous = "";
var next = "";
if(this.props.currentPage > 0){
previous = {this.props.previousText}
}
if(this.props.currentPage != (this.props.maxPage -1)){
next = {this.props.nextText}
}
var options = [];
var startIndex = Math.max(this.props.currentPage - 5, 0);
var endIndex = Math.min(startIndex + 11, this.props.maxPage);
if (this.props.maxPage >= 11 && (endIndex - startIndex) <= 10) {
startIndex = endIndex - 11;
}
for(var i = startIndex; i < endIndex ; i++){
var selected = this.props.currentPage == i ? "current-page-selected" : "";
options.push(<button className={selected} data-value={i} onClick={this.pageChange}>{i + 1}</button>);
}
return (

{previous}

{options}

{next}

)
}
});
Then initialize your component as follows:
<Griddle results={fakeData} tableClassName="table" useCustomFormat="true"
customFormat={OtherComponent} useCustomPager="true" customPager={OtherPager} />
No Data
Griddle will, by default, show a message if there is no data in the result set. There are two ways that it can be customized though.
Basic NoData message
The first way to customize what Griddle does when there is no data is setting the noDataMessage
property.
- noDataMessage
- string - The message that will be displayed when there is no data
Example:
<Griddle noDataMessage={"No data could be found."} />
NoData Component
Outside of the NoData message, Griddle can take a customNoDataComponent
that will be displayed when there are no records.
- customNoDataComponent
- object - The component that will be displayed when there is no data
Example:
var NoDataComponent = React.createClass({
render: function(){
return (

No data is available

You can google for more data

);
}
});
React.render(<Griddle customNoDataComponent={NoDataComponent} />, document.getElementById("some-id"));
Custom Icons
Please see the styling section for custom icons.


 Congrats! You've made an excellent choice! Now the question remains, which track should you chose to study. Although most students find that they will need to learn other languages after they finish at Epicodus it's still important to pick a track that you feel will put your best foot forward. This brief survey can help you decide which track to study.
Find out more about the courses offered at Epicodus by clicking here!
Find out more about the courses offered at Epicodus by clicking here!
Find out more about the courses offered at Epicodus by clicking here!
Find out more about the courses offered at Epicodus by clicking here!
Find out more about the courses offered at Epicodus by clicking here!
Or you can do some more research and find out more about the courses offered at Epicodus by clicking here!


Dear `clientName`,
You are requesting to perform `actionName` action,
Please enter the verification code below in your web page:
`veriCode`


true.
republic.-->
carried
vans
community
soldiers
nothing
russias
voters
claim
referendum
recognise
choice.
quite
do
know
so
armed
did
rightly
{{flagrussian
{{flagrussian
{{flagrussia
{{flagrussia
{{flagrussia
nonprofit
webtitletreaty
accept
{{nowrapcrimean
{{nowraprepublic


{% extends "base.html" %} {% block title %} {{ sitetitle }} :: Assignment Form {% endblock %} {% block extrahead %} {% endblock %} {% block bannertitle %} Training Assignments {% endblock %} {% block pagecontent %}
| Event Title: | The following tags are available to include information from this assignment in calendar events:
[launchurl] - replaced by a link to launch the training
|
|
| Event Description: |
| Email Subject: | The following tags are available to include information from this assignment in emails:
[launchurl] - replaced by a link to launch the training
[fname] - replaced by the invitee's first name
[lname] - replaced by the invitee's last name
[myname] - replaced by your name, as the sender
[duedate] - replaced by the selected due date
|
|
| Email Body: |


Part 6: Enumerations
Now we will extend our schema with a new slot status
which records the vital status of a person, i.e whether they are living or dead.
We could do this by making the range of status
a string, and
allowing the data provider to fill this with any value. However, this
will result in messy data that will need to cleaned up before any analysis is performed, e.g. to harmonize strings such as “alive” and “living”
LinkML allows you to provide enumerations, collections of controlled string values. Here is our example schema, with an example enumeration:
personinfo.yaml:
id: https://w3id.org/linkml/examples/personinfo
name: personinfo
prefixes:
linkml: https://w3id.org/linkml/
schema: http://schema.org/
personinfo: https://w3id.org/linkml/examples/personinfo/
ORCID: https://orcid.org/
imports:
- linkml:types
default_range: string
classes:
Person:
class_uri: schema:Person
attributes:
id:
identifier: true
full_name:
required: true
description:
name of the person
slot_uri: schema:name
aliases:
multivalued: true
description:
other names for the person
phone:
pattern: "^[\\d\\(\\)\\-]+$"
slot_uri: schema:telephone
age:
range: integer
minimum_value: 0
maximum_value: 200
status:
description: >-
vital status of the person
range: PersonStatus ## see "enums" section below
id_prefixes:
- ORCID
Container:
attributes:
persons:
multivalued: true
inlined_as_list: true
range: Person
enums:
PersonStatus:
permissible_values:
ALIVE:
DEAD:
UNKNOWN:
Now let’s collect some data!
data.yaml:
persons:
- id: ORCID:1234
full_name: Clark Kent
age: 33
phone:<PHONE_NUMBER>
status: ALIVE
- id: ORCID:2222
full_name: Count Dracula
status: UNDEAD
We can run our data through the linkml validator (which just wraps a jsonschema validator by default)
linkml-validate -s personinfo.yaml data.yaml
If you run this you should see that it throws an error with the message:
ValueError: Unknown PersonStatus enumeration code: UNDEAD
Let’s go ahead and fix the data, changing Dracula’s vital status from UNDEAD to UNKNOWN:
data-fixed.yaml:
persons:
- id: ORCID:1234
full_name: Clark Kent
age: 33
phone:<PHONE_NUMBER>
status: ALIVE
- id: ORCID:2222
full_name: Count Dracula
status: UNKNOWN
This should now validate successfully:
linkml-validate -s personinfo.yaml data-fixed.yaml
Mapping Enums to vocabularies
We will now enhance our enums and map the different values to terms from vocabularies or ontologies. This doesn’t affect the basic behavior of tools that use the schema - however, it does provide a better basis for schema mapping, and it does affect the behavior of generated RDF.
Here we use the biomedical ontology PATO to provide codes/terms for statuses like living and dead:
personinfo-mapped.yaml:
id: https://w3id.org/linkml/examples/personinfo
name: personinfo
prefixes:
linkml: https://w3id.org/linkml/
schema: http://schema.org/
personinfo: https://w3id.org/linkml/examples/personinfo/
ORCID: https://orcid.org/
PATO: http://purl.obolibrary.org/obo/PATO_
imports:
- linkml:types
default_range: string
classes:
Person:
class_uri: schema:Person
attributes:
id:
identifier: true
full_name:
required: true
description:
name of the person
slot_uri: schema:name
aliases:
multivalued: true
description:
other names for the person
phone:
pattern: "^[\\d\\(\\)\\-]+$"
slot_uri: schema:telephone
age:
range: integer
minimum_value: 0
maximum_value: 200
status:
description: >-
vital status of the person
range: PersonStatus
id_prefixes:
- ORCID
Container:
attributes:
persons:
multivalued: true
inlined_as_list: true
range: Person
enums:
PersonStatus:
permissible_values:
ALIVE:
description: the person is living
meaning: PATO:0001421
DEAD:
description: the person is deceased
meaning: PATO:0001422
UNKNOWN:
description: the vital status is not known
todos:
- map this to an ontology
linkml-convert -s personinfo-mapped.yaml data-fixed.yaml
Outputs:
{
"persons": [
{
"id": "ORCID:1234",
"full_name": "Clark Kent",
"phone": "555-555-5555",
"age": 33,
"status": "ALIVE"
},
{
"id": "ORCID:2222",
"full_name": "Count Dracula",
"status": "UNKNOWN"
}
],
"@type": "Container"
}
Use in Python
(you can skip this section if you are not concerned with interacting with data instances via Python)
gen-python personinfo-mapped.yaml > personinfo.py
then use this code:
test.py:
from personinfo import Person, PersonStatus
person = Person(id='P1', full_name='Julius Caesar', status="DEAD")
print(f'STATUS={person.status}')
print(f'MEANING={person.status.meaning}')
python test.py
Outputs:
STATUS=DEAD
MEANING=http://purl.obolibrary.org/obo/PATO_0001422
Next we will explore more aspects of the modeling language
Further reading
Metamodel Specification
Enum Definition class
FAQ:


Best global live matches
Filter live matches for player username
0; else emptyList" style="padding-top: 20px;">
{{ liveMatch.player1 }}
vs {{ liveMatch.player2 }}
Filter live matches for player username


Dendrogram Expansion/Contraction¶
The expansion or contraction of each node is individually controlled through the ‘+’ or ‘-‘ icons that appear to the left of each node, respectively above or below the line connecting the node to its parent. In Figure 57, the subtree with 58 nodes in Figure 56 has been expanded by clicking the ‘+’. Each expansion adds two levels of the tree to the dendrogram. Note that leaf-level nodes are drawn as dots.
To expand a subtree all the way down to the leaves in a single action, click on the subtree triangle itself. In Figure 58, the subtree with 9 nodes at the top of the dendrogram in Figure 56 has been expanded to the leaf level with this one-click operation. Caution should be exercised when doing a full subtree expansion for subtrees over 20 or so nodes, since the dendrogram can become cluttered and largely unintelligible. Figure 59 demonstrates the results of clicking on the subtree with 110 nodes at the bottom of the dendrogram in Figure 56.
The operation of collapsing nodes reduces all of the nodes below the designated node (the node whose ‘-‘ icon is clicked) into a single subtree, regardless of the number of levels that are currently visible. In Figure 60, the bottom half of the dendrogram in Figure 57 has been collapsed into a single subtree with 166 nodes. This ability to expand and contract sections of the dendrogram controls the level of detail, maximizing the rendered portion of the tree around areas of interest.


{% if name == 'smdsbz' %}
Hi, boss!
{% elif name == 'President Jiang' %}
+1s
{% elif name %}
Hi, {{ name }}! Long time no see!
{% else %}
I don't seem to know you, but hi anyway.
{% endif %}


brew-journal
${auth_errors} ${auth_form.non_field_errors()}
login
${field(auth_form.username)}
${field(auth_form.password)}
create account
${field(auth_form.password_again)}
${field(auth_form.email)}
brew-journal
helps you keep a digital journal of the status and progress of your homebrew beer
The
source for this site is available on github.com
under the
New BSD
license.
recent brews
"${brew.recipe.name}"
A brew
by
${brew.brewer.username}
on ${std.fmt.date.ymd(brew.brew_date, request.user)}
recent recipes
"${recipe_a(recipe)}", added ${std.fmt.date.ymd(recipe.insert_date, request.user)} by
${recipe.author}
.


ShellKit
ShellKit gives you a powerful way to interface to the command line with a set of standard command or you can extend to create your own.
License
This project is licensed under the MIT License - see the LICENSE.md file for details
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Built With
From scratch… like biscuits
Prerequisites
- macOS 10.13 or later
@available(macOS 10.13, *)
Installing
Package Manager
dependencies: [
.package(url: "https://gitlab.com/thecb4/shellkit.git", .branch("master")),
],
Using
ShellKit is used by importing the library and easily leveraging the existing commands
import ShellKit
try Shell.swiftTestGenerateLinuxMain()
Testing
ShellKit does come with tests
swift test
Roadmap and Contributing
Versioning
We use SemVer for versioning. For the versions available, see the tags on this repository.
Roadmap
Please read ROADMAP for an outline of how we would like to evolve the library.
Contributing
Please read CONTRIBUTING.md for details on our code of conduct, and the process for changes to us.
Changes
Please read CHANGELOG for details on changes to the library.
Authors
- ‘Cavelle Benjamin’ - thecb4
See also the list of contributors who participated in this project.
Acknowledgments
- Hat tip to anyone whose code was used
- Inspiration
- etc


This library does not deploy PyPI at this moment. So please clone and install yourself.
clone from repository
git https://github.com/muilab/mui-display-lib.git
library use Cython for make C-object from pyx file.
install Cython
python3 -m pip install Cython
build module
python3 setup.py build_ext --inplace
install library
python3 setup.py install


Hi power user, you can use theses shortcuts to be more productive in Gospeak
Navigation
?Hotkey help
/Focus search
g hGo to your Home
g gGo to your first Group
g pGo to Public page
g sGo to Settings
1 or 2 or 3...Navigate to menu X
shift+rightNext page
shift+leftPrevious page
Actions
+Toggle create button


[1]
Does Not Work; Does Not Work
[2]
cheap price; cheap quality
[3]
quiet, huge capacity, elegant design; slightly expensive


Guess the whole proposed trend starting from the last point of the blue line. Drag to draw your trend from left to the right, using background color and cursor pointer to find the best path. A red background means you are far from the real trend, a green one you are close to it, and the arrow pointer suggests you the right direction (up or down) to better follow it. At the end you can verify your ability and gain real $$$ from the company you challenged. Bazinga!
Historical stock data by IEX.


Random Acts
Mission Statement Mission Statement Mission Statement
Let us help you do all the nice things you
have always wanted to do
About
Meet the team behind Random Acts
User Stories
FAQ
Contact Us
Menu
Home
About
Meet Us
Stories
FAQ
Contact
Log In


Category: {{business.category}}
Years In Business: {{business.year_in_business}}
{{business.description}}


AP Calculus Practice Exam
AB Version - Section I - Part A
Calculators ARE NOT Permitted On This Portion Of The Exam
28 Questions - 55 Minutes
1) Give f(g(1)), given that
a)
b)
c)
d)
e)
2) Find the slope of the tangent line to the graph of f at x = 4, given that
a)
b)
c)
d)
e)
3) Determine
a)
b)
c)
d)
e)
4) LetA region is bounded between the graphs of y = -1 and y = f(x) for x between -1 and 0, and between the graphs of y = 1 and y = f(x) for x between 0 and 1. Give an integral that corresponds to the area of this region.
a)
b)
c)
d)
e)
5) Given that Determine the change in y with respect to x.
a)
b)
c)
d)
e)
6) Compute the derivative of
a)
b)
c)
d)
e)
7) Compute
a)
b)
c)
d)
e)
8) Determine
a)
b)
c)
d)
e)
9) Give the equation of the normal line to the graph of at the point ( 0 , 2 ).
a)
b)
c)
d)
e)
10) Determine the concavity of the graph of at x = .
a)
b)
c)
d)
e)
11) Compute
a)
b)
c)
d)
e)
12) Give the value of x where the functionhas a local maximum.
a)
b)
c)
d)
e)
13) The slope of the tangent line to the graph of at x = 0 is 4. Give the value of c.
a)
b)
c)
d)
e)
14) Compute
a)
b)
c)
d)
e)
15) What is the average value of the functionon the interval from x = -3 to x = -1?
a)
b)
c)
d)
e)
16) Compute
a)
b)
c)
d)
e)
17) Find the instantaneous rate of change of at t = 0.
a)
b)
c)
d)
e)
18) Compute
a)
b)
c)
d)
e)
19) A solid is generated by rotating the region enclosed by the graph of the lines x = 1, x = 2, and y = 1, about the x-axis. Which of the following integrals gives the volume of the solid?
a)
b)
c)
d)
e)
20) Compute
a)
b)
c)
d)
e)
21) Given y > 0 and If the point is on the graph relating x and y, then what is y when x = 0?
a)
b)
c)
d)
e)
22) Determine
a)
b)
c)
d)
e)
23) Determine
a)
b)
c)
d)
e)
24) A particle's acceleration for t > 0 is given by The particle's initial position is 2 and its velocity at t = 1 is 5. What is the position of the particle at t = 2?
a)
b)
c)
d)
e)
25) Determine
a)
b)
c)
d)
e)
26) Determine the derivative of at x = /2.
a)
b)
c)
d)
e)
27) Compute the derivative of
a)
b)
c)
d)
e)
28) Determine
a)
b)
c)
d)
e)


Yuna
-
Does She
[by:Trap_Girl] 作曲 : Yunalis Zarai/R. Braun/Sam Farrar/Michelle Buzz/Rami Jrade/Jay Park 作词 : Yunalis Zarai/R. Braun/Sa... -
Shine Your Way
Just before the dawn, When the light's still gone, Shine, shine your way, And you may not know, where to go, Shine, shi... -
No Different
作曲 : Tablo/Code Kunst/Ilsey Juber 作词 : Tablo/ILSEY JUBER You say I’ve changed. I’m acting strange. You say I’ve cha... -
Leaving
[by:是非又欲] [ti:] [ar:] [al:] [by:九九Lrc歌词网～www.99Lrc.net] I don't really wanna try and change your mind If you can't find... -
Lullabies
[by:一西海岸] [ti:Lullabies] [ar:Yuna] [al:] [by:九九Lrc歌词网～www.99Lrc.net] Like lullabies you are, Forever in my mind. I see y... -
Colors
[by:机智良儿] [ti:Colors] [ar:Yuna] [al:] [by:阿良] I'm lying on the floor. I'm drowning from the storm you make. We both know... -
Strawberry letter 23
[by:現充少年路小馬] [ti:] [ar:] [al:] [by:] [offset:0] Hello, my love I heard a kiss from you Red magic satin playing near too ... -
Baby Boy (Famba Remix)
[by:小陀螺Tony] Oh no, it's happening for real You said we'd make it work but you couldn't let it heal No point in dropping... -
Crush
[by:IM花花] You call me on a lazy afternoon Asking me what I'm up to Let's find something to do Baby I'd be down, down, do... -
Bad Idea
作词 : WILLIAMS, PHARRELL/ZARAI, YUNALIS Da da dum... What is this I feel about you Hoping I don't make it ... -
Mannequin
[by:SebastianStan] Who are you? What you want from me? I've got nothing to give Are you here to waste time with me? 'Cau... -
Don't Call Me
[by:不醉不归无烟不欢] Call me Don't call me Call me Don't call me Hey I never thought this love could go so wrong And so it did ... -
Forevermore
[by:Trap_Girl] 作曲 : Yunalis Zaraai/Robin Hannibal/Shungudzo Kuyimba Mmmmmmmm Mmmmmmmm Mmmmmmmm Mmmmmmmm I feel the fire... -
Right Again
It takes two this time To make this right Should I start again And end this fight It's easy to pretend That we're fine... -
Favourite Thing
It's in the way you drink your coffee And how you have faith in me And you love the camera And you tell me that I'm good... -
I Wanna Go
[by:Andy_仲夏] He's one in a million I don't know when I'll be seeing him again He wants to live in a city Move out there ... -
Someone Out of Town
[by:内个LM杀很大] [ti:Someone out of town] [ar:Yuna] [al:Terukir Di Bintang] [by:Stefan] Oh my god I see you clearly now In m... -
Modern Flame
[by:Where-is-he] [ti:] [ar:] [al:] [by:九九Lrc歌词网～www.99Lrc.net] Take away the pain Tear me down in flames Touch me like y... -
Best Love
[by:-慶-] I need to know about this, babe Cause I can't sleep at night The conversation that we had Something just fe... -
Live Your Life
[by:chuyulun-] Find your lie, don't hide from what you are And rise before you fall, And hope for something more Live if...  As a friend, as a friend, as an old Enemy. Take your time, hurry up, ... -
Lanes
[by:井米] 作曲 : Yunalis Zarai/Robin Braun/Paul Salva 作词 : Yunalis Zarai/Robin Braun/Paul Salva Why do you keep tellin' me... -
Amy
[by:yvvoonnnnee] 作曲 : Yunalis Mat Zara’ai/Brian Warfield/Maclean Robinson/Micah Davis 作词 : Yunalis Mat Zara’ai/Brian W... -
Used To Love You
[by:-慶-] I wonder if you think about me Now that you're out there on your own You made it seem so easy I wanna be ju... -
Island
You're an island, I'm just a little boat I'm heading somewhere I don't even know Hope you don't mind me stopping by your... -
A Whole New World (From "Aladdin")
[by:莎士比亚书店] I can show you the world Shining shimmering splendid Tell me princess now when did You last let your heart d... -
Flags
[by:bbbmylve] They say we shouldn't be together But nothing could be better Old rules trying to box us in They don't wan... -
Best Of Me
[by:-慶-] I can see that lately you've been struggling I know that you've been de-motivated Saying, "Sometimes they sa... -
The Essence of Asia
[by:圈圈圈粉儿] You'll love Malaysia now and forever Different races everywhere The soul of Asia is surely here This beautifu...  -
(Not) The Love Of My Life
[by:Trap_Girl] 作曲 : Yunalis Mat Zara’ai/Robin Hannibal/A.s. Govere 作词 : Yunalis Mat Zara’ai/Robin Hannibal/A.s. Govere... -
Places To Go
[by:Jessxc] [ti:Places To Go] [ar:Yuna] [al:Places To Go] [by:] [offset:0] Places To Go - Yuna Written by：Yunalis Zarai/... -
Your Love
I've looked around the world The sky and all the Universe For a love that's pure & true So I can forget about you I... -
Lights And Camera
Lights and Camera - Yuna Lights Camera Struck a pose And if someone help you Lipstick on Yeah High heels higher Six feet... -
Planes
[by:mm最爱汤泡饭] Driving fast but I am no where I'm at home when I am out there I speak louder when I'm silent Trynna be som... -
Pulang
[by:Karlek] [ti:pulang] [ar:yuna, sonaone] [al:pulang] [by:karlek] 作曲 : Yuna/AG Coco 作词 : Yuna/SonaOne Tak usahlah kau...  I think you're doing fine That girl in... -
Unrequited Love
Only when I look into your eyes My senses ignite I feel life I was waiting for an answer Like a feather falling Fr... -
Too Close
[by:几谦] [ti:Too Close] [ar:Yuna] [al:Chapter(Deluxe)] [by:几谦] ooh ooh ha.. Fighting this feeling like I won And I'm too ... -
Fading Flower
[by:我遮住脸也是挺帅的] [ti:] [ar:] [al:] [by:九九Lrc歌词网～www.99Lrc.net] You got that walk walk walk Got that talk talk talk Got tha... -
Blank Marquee
[by:Trap_Girl] 作曲 : Yunalis Mat Zara’ai/R. Braun/CHLOE ANGELIDES/Gerald Gillum 作词 : Yunalis Mat Zara’ai/R. Braun/CHLOE... -
Pink Youth
[by:Trap_Girl] 作曲 : Yunalis Mat Zara’ai/R. Braun/Jason Pounds/CHLOE ANGELIDES/Joel Van Dijk/Simbi Ajikawo 作词 : Yunalis... -
Better Now
[by:是非又欲] [ti:] [ar:] [al:] [by:九九Lrc歌词网～www.99Lrc.net] You prolly think that you are better now, better now You only s... -
Time
Where do I begin? It was 1993 when I was maybe six or seven And my mom and me was driving Listening to music I've be... -
All I Do
[by:几谦] [ti:All I Do] [ar:Yuna] [al:Chapters(Deluxe）] Is think about you Sleep late and wake up then I'll just stay in m... -
Castaway
[by:Trap_Girl] 作曲 : Yunalis Mat Zara’ai/R. Braun/CHLOE ANGELIDES/Tyler Okonma 作词 : Yunalis Mat Zara’ai/R. Braun/CHLOE ... -
Teenage Heartbreak
[by:Trap_Girl] 作曲 : Yunalis Mat Zara’ai/Jason Pounds/Miyavi 作词 : Yunalis Mat Zara’ai/Jason Pounds/Miyavi If someone wa... -
Likes
[by:Trap_Girl] 作曲 : Yunalis Mat Zara’ai/R. Braun/Carl McCormick/Kyle Harvey 作词 : Yunalis Mat Zara’ai/R. Braun/Carl McC... -
Falling
It's been a long day I can't seem to keep up with it I want to fall asleep So I don't have to remember, Don't have to ca...


Adapted from
https://wtfleming.github.io/2015/02/19/d3-force-directed-graph/
If you see me, something's broken =(