Table of Contents
This document explains how to extend Boost.Build to accomodate your local requirements. Let's start with a simple but realistic example.
Say you're writing an application that generates C++ code. If you ever did this, you know that it's not nice. Embedding large portions of C++ code in string literals is very awkward. A much better solution is:
It's quite easy to achieve. You write special verbatim files
that are just C++, except that the very first line of the file
contains the name of a variable that should be generated. A simple tool
is created that takes a verbatim file and creates a cpp file with
a single char*
variable whose name is taken from the first line
of the verbatim file and whose value is the file's properly quoted content.
Let's see what Boost.Build can do.
First off, Boost.Build has no idea about "verbatim files". So, you must register a new target type. The following code does it:
import type ; type.register VERBATIM : vrb ;
The first parameter to
type.register
gives the name of the
declared type. By convention, it's uppercase. The second parameter
is the suffix for files of this type. So, if Boost.Build sees
code.vrb
in a list of sources, it knows that it's of type
VERBATIM
.
Next, you tell Boost.Build that the verbatim files can be
transformed into C++ files in one build step. A
generator is a template for a build step that
transforms targets of one type (or set of types) into another. Our
generator will be called verbatim.inline-file
; it
transforms VERBATIM
files into CPP
files:
import generators ; generators.register-standard verbatim.inline-file : VERBATIM : CPP ;
Lastly, you have to inform Boost.Build about the shell
commands used to make that transformation. That's done with an
actions
declaration.
actions inline-file { "./inline-file.py" $(<) $(>) }
Now, we're ready to tie it all together. Put all the code
above in file verbatim.jam
, add import verbatim ;
to project-root.jam
, and it's possible to write
the following in Jamfile:
exe codegen : codegen.cpp class_template.verbatim usage.verbatim ;
The verbatim files will be automatically converted into C++ and linked it.
In the subsequent sections, we will extend this example, and review
all the mechanisms in detail. The complete code is available in example/customization
directory.


-1- IN THE HIGH COURT OF KARNATAKA AT BENGALURU DATED THIS THE 9TH DAY OF SEPTEMBER, 2019 PRESENT THE HON'BLE MR.ABHAY S. OKA, CHIEF JUSTICE AND THE HON'BLE MR.JUSTICE MOHAMMAD NAWAZ WRIT PETITION NO.35737 OF 2019 (GM-POL) BETWEEN: SRI. S. RAMESH KUMAR S/O H SRINIVASA REDDY AGED ABOUT 38 YEARS RESIDING AT NO.150 HALANAYAKANAHALLI VILLAGE VARTHUR HOBLI SARJAPUR MAIN ROAD CARLELRAM POST BENGALURU - 560 035 ... PETITIONER (BY SMT. ANUSHA NANDISH, ADVOCATE) AND: 1. KARNATAKA STATE POLLUTION CONTROL BOARD REPRESENTED BY ITS CHAIRMAN PARISARA BHAVAN, 1 TO 5 FLOORS NO.49, CHURCH STREET BENGALURU - 560 001 2. BANGALORE ELECTRICITY SUPPLY COMPANY LIMITED (BESCOM) REPRESENTED BY ITS MANAGING DIRECTOR K.R. CIRCLE, BENGALURU - 560 001 ... RESPONDENTS --- THIS WRIT PETITION IS FILED UNDER ARTICLE 226 & 227 OF THE CONSTITUTION OF INDIA, PRAYING TO QUASH THE CORRIGENDUM DTD.12.07.2019 PASSED BY THE R-1, AT (ANNEXURE-A) AND CONSEQUENTLY ALLOW THE ABOVE PETITION AND ETC. -2- THIS PETITION COMING ON FOR PRELIMINARY HEARING THIS DAY, CHIEF JUSTICE MADE THE FOLLOWING: ORDER
The impugned corrigendum modifies the direction issued earlier under Section 33(A) of the Water (Prevention and Control of Pollution) Act, 1974 (for short 'the said Act'). Therefore, the impugned corrigendum dated 12th July 2019 is also issued in exercise of power under Section 33(A) of the said Act.
2. The remedy of preferring a substantive appeal under sub-section (c) of Section 33-B of the said Act is available to the petitioner. The contentions which are raised in the petition can be always agitated in the appeal. On the ground of availability of an efficacious remedy, we decline to entertain this petition and the same is accordingly disposed of. The remedy of the petitioner is kept open.
Sd/-
CHIEF JUSTICE Sd/-
JUDGE AHB


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10.D, 1.3.1-list-type
| Test Name | Test ID | Test Condition |
|---|---|---|
| 1.3.1-list-type | 10.D | All visually apparent lists are programmatically identified according to their type. |
Evaluate Results:
If ALL the following are TRUE, the content PASSES:
- All content that has the visual appearance of a list is defined programmatically as a list, according to the type of list.
- An unordered list (with or without bullets) is marked as an unordered list (ul).
- An ordered list is marked as an ordered list (ol).
- Terms and their descriptions that are presented in the form of a list are marked as a description list (dl)
- All programmatic list relationships, including nesting and hierarchies, are consistent with the list relationships presented visually.


001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one 003 * or more contributor license agreements. See the NOTICE file 004 * distributed with this work for additional information 005 * regarding copyright ownership. The ASF licenses this file 006 * to you under the Apache License, Version 2.0 (the 007 * "License"); you may not use this file except in compliance 008 * with the License. You may obtain a copy of the License at 009 * 010 * http://www.apache.org/licenses/LICENSE-2.0 011 * 012 * Unless required by applicable law or agreed to in writing, 013 * software distributed under the License is distributed on an 014 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY 015 * KIND, either express or implied. See the License for the 016 * specific language governing permissions and limitations 017 * under the License. 018 */ 019 020 package org.apache.commons.compress.archivers.zip; 021 022 import java.io.IOException; 023 import java.nio.ByteBuffer; 024 025 /** 026 * An interface for encoders that do a pretty encoding of ZIP 027 * filenames. 028 * 029 *
There are mostly two implementations, one that uses java.nio 030 * {@link java.nio.charset.Charset Charset} and one implementation, 031 * which copes with simple 8 bit charsets, because java-1.4 did not 032 * support Cp437 in java.nio.
 033 * 034 *
The main reason for defining an own encoding layer comes from 035 * the problems with {@link java.lang.String#getBytes(String) 036 * String.getBytes}, which encodes unknown characters as ASCII 037 * quotation marks ('?'). Quotation marks are per definition an 038 * invalid filename on some operating systems like Windows, which 039 * leads to ignored ZIP entries.
 040 * 041 *
All implementations should implement this interface in a 042 * reentrant way.
 043 */ 044 public interface ZipEncoding { 045 /** 046 * Check, whether the given string may be losslessly encoded using this 047 * encoding. 048 * 049 * @param name A filename or ZIP comment. 050 * @return Whether the given name may be encoded with out any losses. 051 */ 052 boolean canEncode(String name); 053 054 /** 055 * Encode a filename or a comment to a byte array suitable for 056 * storing it to a serialized zip entry. 057 * 058 *
Examples for CP 437 (in pseudo-notation, right hand side is 059 * C-style notation):
 060 *
 061 * encode("\u20AC_for_Dollar.txt") = "%U20AC_for_Dollar.txt" 062 * encode("\u00D6lf\u00E4sser.txt") = "\231lf\204sser.txt" 063 *
 064 * 065 * @param name A filename or ZIP comment. 066 * @return A byte buffer with a backing array containing the 067 * encoded name. Unmappable characters or malformed 068 * character sequences are mapped to a sequence of utf-16 069 * words encoded in the format %Uxxxx. It is 070 * assumed, that the byte buffer is positioned at the 071 * beginning of the encoded result, the byte buffer has a 072 * backing array and the limit of the byte buffer points 073 * to the end of the encoded result. 074 * @throws IOException 075 */ 076 ByteBuffer encode(String name) throws IOException; 077 078 /** 079 * @param data The byte values to decode. 080 * @return The decoded string. 081 * @throws IOException 082 */ 083 String decode(byte [] data) throws IOException; 084 }


Draws shape
samples from each of the given Gamma distribution(s).
tf.compat.v1.random.gamma
tf.compat.v1.random_gamma
tf.compat.v2.random.gamma
tf.random.gamma( shape, alpha, beta=None, dtype=tf.dtypes.float32, seed=None, name=None )
alpha
is the shape parameter describing the distribution(s), and beta
is
the inverse scale parameter(s).
Note: Because internal calculations are done using float64
and casting has
floor
semantics, we must manually map zero outcomes to the smallest
possible positive floating-point value, i.e., np.finfo(dtype).tiny
. This
means that np.finfo(dtype).tiny
occurs more frequently than it otherwise
should. This bias can only happen for small values of alpha
, i.e.,
alpha << 1
or large values of beta
, i.e., beta >> 1
.
The samples are differentiable w.r.t. alpha and beta. The derivatives are computed using the approach described in the paper
Michael Figurnov, Shakir Mohamed, Andriy Mnih. Implicit Reparameterization Gradients, 2018
samples = tf.random.gamma([10], [0.5, 1.5]) # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents # the samples drawn from each distribution samples = tf.random.gamma([7, 5], [0.5, 1.5]) # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] # represents the 7x5 samples drawn from each of the two distributions alpha = tf.constant([[1.],[3.],[5.]]) beta = tf.constant([[3., 4.]]) samples = tf.random.gamma([30], alpha=alpha, beta=beta) # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions. loss = tf.reduce_mean(tf.square(samples)) dloss_dalpha, dloss_dbeta = tf.gradients(loss, [alpha, beta]) # unbiased stochastic derivatives of the loss function alpha.shape == dloss_dalpha.shape # True beta.shape == dloss_dbeta.shape # True
shape
: A 1-D integer Tensor or Python array. The shape of the output samples
to be drawn per alpha/beta-parameterized distribution.alpha
: A Tensor or Python value or N-D array of type dtype
. alpha
provides the shape parameter(s) describing the gamma distribution(s) to
sample. Must be broadcastable with beta
.beta
: A Tensor or Python value or N-D array of type dtype
. Defaults to 1.
beta
provides the inverse scale parameter(s) of the gamma
distribution(s) to sample. Must be broadcastable with alpha
.dtype
: The type of alpha, beta, and the output: float16
, float32
, or
float64
.seed
: A Python integer. Used to create a random seed for the distributions.
See
tf.compat.v1.set_random_seed
for behavior.name
: Optional name for the operation.samples
: a Tensor
of shape
tf.concat([shape, tf.shape(alpha + beta)], axis=0)
with values of type
dtype
.


{% extends 'base.html' %} {% block content %}
Title: {{ eve }}
Owner: {{ uid }}
Date: {{ date }}
Time: {{ time }}
Location: {{ location }}
Description: {{ description }}


fruit
fruit priceFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
| Features |
|---|
| herbicides |
| insecticides |
| fungicides |
| pesticides |
| pesticides |
| pesticides |
| pesticides |
| cattle feed |
fruit
fruit priceFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
fruit
fruit priceFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
fruit
fruit priceFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
fruit
fruit priceFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
fruit
fruit priceFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.


try{} catch (IOException|RuntimeException e) {...}
The quick fix splits multi-catch section into separate catch blocks.
For example, the multi-catch section
try{}
catch (IOException|RuntimeException e) {
/*handle the exception*/
}
is transformed into the following:
try{}
catch (IOException e) {
/*handle the exception*/
}
catch (RuntimeException e) {
/*handle the exception*/
}


{% load static %}
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-Sally Kempton


RGV announces 'Dadas Of Hyderabad' - Telugu News - IndiaGlitz.com
Ram Gopal Varma has announced a new project. It's a film titled 'Dadas Of Hyderabad'. Sandeep Madhav of 'Vangaveeti' and 'George Reddy' fame will be the hero.
"Just signed Sandeep Madhav for DADAS OF HYDERABAD. After finishing the rowdies of Vijayawada and factionists in Rayalaseema, I am making a film on the DADAs of the ’80s in Hyderabad. The story is based on a real-life character, which was my inspiration for the character of SHIVA," RGV said, reminding us of 'Shiva' for the millionth time in his career.
RGV awaits the release of the political fiction 'Kamma Rajyamlo Kadapa Reddlu' on November 29.
Trending News
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Sumanth's film is titled 'Kapatadhaari'
George Reddy was anti-social: ABVP leader
'Raja Vaaru Rani Gaaru' Trailer: A village-based love story
Raj Madiraju's 'Sira' unveiled
Rajinikanth wraps up dubbing for 'Darbar'
Breaking! Srikanth Addala to direct Venky's next
Allu Arjun's 'whopping' demand makes news
Politicians were stunned by her ageless beauty: Chiranjeevi
Sai Pallavi, Rashmika in the same zone
I wanted to make a George Reddy biopic: Sandeep Reddy
It's about who walks with you: Nayanthara
Headlines
Swami Nithyananda’s institute has kidnapped our daughters: Parents
Update your WhatsApp to avoid hacking
Telugu realtor Lingamaneni Ramesh denies insolvency reports
BJP, other parties should learn from NCP, BJD: Modi
Tap water quality in Hyderabad is India's second best
Boy rapes cousin by tying up her hands & legs
Modi for 'One Nation, One Ration Card', & 'One Nation, One Pay Day'
#IWantMyMasjidBack trends on Twitter
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'Pregnant' woman is pregnant with narcotics!


Detect valleys and peaks in stockmarket data
Facebook
In the following example we load the 2016 elections data of the USA for various candidates. We will check whether the votes are fraudulent based on benfords distribution.
# Import library
from caerus import caerus
# Initialize
cs = caerus()
# Import example dataset
X = cs.download_example(name='facebook')
# Fit
cs.fit(X)
# Plot
cs.plot()
# Results are stored in the object
print(cs.results.keys())
# ['X', 'simmat', 'loc_start', 'loc_stop', 'loc_start_best', 'loc_stop_best', 'agg', 'df']
# Results are stored in DataFrame
print(cs.results['df'])
# X labx peak valley
# 0 38.2318 0 False False
# 1 34.0300 0 False False
# 2 31.0000 0 False False
# 3 32.0000 0 False False
# 4 33.0300 0 False False
# ... ... ... ...
# 1467 169.3900 0 False False
# 1468 164.8900 0 False False
# 1469 159.3900 0 False False
# 1470 160.0600 0 False False
# 1471 152.1900 0 False False
[1472 rows x 4 columns]
Bitcoin
For demontration purposes, we will detect the peaks and valley of the Bitcoin. It can be seen that the method easily pickups the peaks and valleys at the early years, and the last years.
# Import library
from caerus import caerus
# Initialize
cs = caerus()
# Import example dataset
X = cs.download_example(name='bitcoin')
# Fit
cs.fit(X)
# cs.fit(X[-300:])
# Plot
cs.plot()
Threshold effect
The threshold will change the strength of peaks and valleys that detected (middle plot). The higher the threshold cut-off, the better the peaks and valleys.
# Set the threshold higher
# Top figure
cs = caerus(threshold=0.025)
# Bottom figure
cs = caerus(threshold=0.9)
# Search last 300 datapoints
cs.fit(X[-300:])
# Plot
cs.plot()
Window effect
The window
size is used to determine the change in percentages. It is computed by the distance of start location + window.
A smaller window size is able to pickup better local minima, and larger window sizes will stress more on the global minma.
See below a demontration where the window size is increased. The figures clearly shows (top figures) that the windows are larger as the detected regions become more horizontal.
# Change the window size
cs = caerus(window=50)
cs = caerus(window=100)
cs = caerus(window=200)
# Search last 300 datapoints
cs.fit(X[-300:])
# Plot
cs.plot()
Gridsearch
With the gridsearch it is possible to automatically search across various windows (window
) and percentages (minperc
).
# Initialize
cs = caerus()
# Gridsearch parameters
cs.gridsearch(X)
# Change search window and minperc
# cs.gridsearch(X, window=np.arange(50,550,100), minperc=np.arange(1,20,5))
# Plot
cs.plot()


Partner accounts allow you to restrict who is allowed to submit galleries at your TGP and/or give certain users special privileges such as being allowed to submit more galleries than a regular submitter. This document will give you an overview of how to add, maintain, and remove partner accounts.
Searching Partners
To search the current partners in your database so you can modify or delete them, access the Partners > Manage Partners function from the control panel menu. This will display a page where you will be able select a number of different search options so you can quickly locate the partner(s) you want to work with. All of the database fields that you can search use the software's Standard Search function. In the table that displays the partners, you will find several functions that you can run on a single partner at a time. Each of those functions are described below.
| Click this icon to suspend the partner account; this will set the partner's status to suspended. | |
| Click this icon to re-activate a suspended partner; this will set the partner's status back to active. | |
| Click this icon to edit the partner. A window will appear where you will be able to edit all of the partner settings. | |
| Click this icon to send an e-mail to the partner. A window will appear where you will enter the e-mail text. | |
| Click this icon to delete this partner. |
| Delete | Use this function to bulk delete partners |
| Use this function to bulk e-mail partners. A window will appear where you will be able to enter the e-mail text. | |
| Suspend | Use this function to bulk suspend partners; the partner account status will be changed to suspended |
| Activate | Use this function to bulk re-activate suspended partners; the partner account status will be changed to active |
| Selected Partners | Apply this function only to the partners that you selected by checking their associated checkbox in the search results |
| All Matching Partners | Apply this function to all of the partners that match the search values you have specified |
| All Partners | Apply this function to all partners in the database |
Adding a Partner
To add a new partner access the Partners > Add Partner function from the control panel menu. This will display a popup window where you will be able to enter the partner account data that you want to use. Each of the form fields are described below.
| Username | The username for this partner |
| Password | The password for this partner |
| Name | The name of the partner account holder |
| E-mail Address | The e-mail address that should be associated with this partner account |
| Weight | The weight value assigned to this account; galleries submitted through this account will be assigned this weight |
| Galleries Per Day | The maximum number of galleries this account is allowed to submit per day |
| Status | The status to assign to this account |
| Categories | Select the categories this partner account should be allowed to submit galleries to, or select ALL CATEGORIES to allow them to submit to any of your categories. |
|
Use the above selected categories as an exclusion list If you would rather select the categories that the partner is NOT allowed to submit galleries to, check this box. When this is checked, the partner will be able to submit to all of your categories EXCEPT for those listed in the Categories setting. |
|
| Start Date | The date and time when this account can begin submitting galleries (must be in YYYY-MM-DD HH:MM:SS format) |
| End Date | The date and time when this account will no longer be able to submit galleries (must be in YYYY-MM-DD HH:MM:SS format) |
| Icons | Below the End Date field you will see your currently defined icons (if any). Put a checkbox next to any of the icons that you want to be automatically assigned to galleries submitted by this partner account. |
|
Allow URL redirection By default galleries that redirect by using 30x HTTP status codes are rejected. To allow this partner to submit galleries that redirect, check this box. |
|
|
No reciprocal link required Check this box to allow this partner to submit galleries that do not contain a reciprocal link |
|
|
Auto-approve galleries Check this box to have galleries submitted by this account be automatically approved |
|
|
No confirmation e-mail required Check this box to allow this partner to submit galleries without confirming them through e-mail |
|
|
Allow blacklisted items Check this box to allow this partner to submit galleries that contain blacklisted data |
Partner Account Status
When partner accounts are created they are assigned a status which determines if the user is able to submit galleries with the account. Each of the available status options are described below.
| Pending | New accounts (generally from the partner account request form) that have not yet been approved |
| Active | Accounts that are active and can submit galleries |
| Suspended | Accounts that have been suspended by an administrator; the user will not be able to submit galleries |
Reviewing Partner Account Requests
Using the partner.php interface gallery owners will be able to request a partner account at your TGP. Every so often you will need to login to the TGPX control panel to process partner account requests. To view the currently waiting partner accounts access the Partners > Review Partner Requests function from the control panel menu. This will display an interface where you will be able to view and process the partner account requests that have been submitted. The information displayed for each account is identical to when you add an account, with the following two extra items:
| IP Address | The IP address of the user that submitted the partner account request |
| Rejection | If you are rejecting the account request, you can select a rejection e-mail message to send to the submitter. Select NONE to have no e-mail message sent. |
All approved accounts will have a password automatically generated for them and the e-mail message defined by the email-partner-added.tpl template will be sent to the user that requested the partner account.
Rejecting partner account requests works exactly like approving them except that you select the Reject option from the first drop down selection field at the bottom of the page. If you select something other than NONE for the Rejection field, the contents of that rejection e-mail message will be sent to the user that requested the partner account.
Processing Inactive Partner Accounts
TGPX includes a couple of functions to process inactive accounts - accounts that have not submitted a gallery in several days. To process inactive partners, access the Partners > Manage Partners function from the control panel menu. Near the top of the page in the header bar that reads "Partner Accounts" click on the icon. This will display a popup window where you will be able to either e-mail or delete inactive accounts.
To send an e-mail message to inactive partner accounts, enter the number of days that the account must have been inactive for and then press the E-mail Accounts button. This will send the contents of the email-partner-inactive.tpl e-mail template to all inactive accounts.
To delete inactive partner accounts, enter the number of days that the account must have been inactive for and then press the Delete Accounts button. This will delete the partner accounts and all of their associated galleries.
Editing a Partner
To edit a partner account you will first need to locate it through the Searching Partners interface. When you locate the account you want to edit, click on the icon to bring up the account editing interface. This interface is nearly identical to the account creation interface, and has the same form fields with the same properties. You can make the changes that you want, and then press the Update Account button to save your changes.

To delete a partner you will first need to locate it through the Searching Partners interface. When you locate the account you want to delete, click on the icon, which will ask you to confirm the deletion.


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{% extends "commons/layout.html" %} {% from "commons/components/card_status.html" import card_status with context %} {% from "commons/components/card.html" import card with context %} {% from "commons/forms/input.html" import form_input with context %} {% block title %}{{ title }}{% endblock %} {% block h1 %}{{ title }}{% endblock %} {% block content %} {% if model.module_interpretability is defined %}


+++ draft = false date = "2007-06-05T17:45:49-03:00" title = "Updates to Zope & Plone tips section" tags = ["rants"] created = "2007-06-05T17:43:08-03:00" modified = "2007-06-05T17:47:22-03:00" permaLink = "http://play.pixelblaster.ro/blog/archive/2007/06/05/updates-to-zope-plone-tips-section" author = "Tiberiu Ichim" aliases = [ "/blog/archive/2007/06/05/updates-to-zope-plone-tips-section" ] +++
I've updated the Zope & Plone tips and tutorials section with a bunch of new pages and several additions to the svn.zope.org package listing. I thought it might be interesting to highlight this here, I'm not exactly sure how visible (or interesting!) is that section to the readers of this blog.


This important port is one of the cleanest in India. It lies on the west coast, about 400nm north of Kochi.
Position 12° 50'39"N, 074° 49'09"E (harbour entrance)


{% trans "Your application has been successfully submitted!" %}
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What to expect next:
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In some cases, Buffalo Water may ask you to send them additional proof of your
situation. You can upload additional documents at getwaterwisebuffalo.org/later-documents/
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You’ll receive a discount on your next water bill if approved.
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It looks like that password passes the Digital Identity Guidlines.


Man pleads guilty in NYC punch that led to a fall and death
by The Associated PressNEW YORK — An Irish man has pleaded guilty to assault for punching another man who fell and died outside a New York City bar.
Court records show Steven O’Brien pleaded guilty Thursday in the encounter that led to John McGee’s death last Thanksgiving. The 21-year-old McGee went by Danny and grew up in Ireland.
WPIX-TV reports the 26-year-old O’Brien apologized to McGee’s parents in a Queens courtroom Thursday.
Authorities said the two men argued and O’Brien punched McGee in the head. McGee fell and hit his head on the pavement.
He was found lying on the ground and was pronounced dead at a hospital
O’Brien is from Dublin. As a child, the American-born McGee moved to the Irish village of Drumlish.
WPIX says O’Brien is expected to get a six-month sentence.
The Associated Press


This RTCMultiConnection demo tries to share files using File.js
You can write cordova/ionic/phonegap based iOS or Android apps for this demo as well.
You can run same demo on Safari-11 as well. (both on MacOSX & iOS)
Microsoft Edge is also supported.


--- layout: archive permalink: /hundreddaysproject/ title: "100 Days Project" author_profile: true published: false ---
during which I attempt to create 100 pen + copic drawings
{% include base_path %} {% for post in site.tags.100daysproject %} {% include archive-single.html type="grid" %} {% endfor %}


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Conisder the observed episodes below :
| A, | south, | C, | $E1_A_SOUTH_C_R |
| C, | south, | $C_NS1 | $C_NS1_R |
| $E1_T3_NS, | exit, | X, | $E1_T3_NS_R |
| B, | east, | C, | $E2_B_EAST_C_R |
| C, | south, | $C_NS2, | $C_NS2_R |
| $E2_T3_NS, | exit, | X, | $E2_T3_NS_R |
| B, | east, | C, | $E3_B_EAST_C_R |
| C, | south, | $C_NS3, | $C_NS3_R |
| $E3_T3_NS, | exit, | X, | $E3_T3_NS_R |
| A, | south, | C, | $E4_A_SOUTH_C_R |
| C, | south, | $C_NS4, | $C_NS4_R |
| $E4_T3_NS, | exit, | X, | $E4_T3_NS_R |
To find \(\hat{V}^\pi(A) \) we first look at all the episodes to find instances where we are on State A. These instances can be found in episodes 1 and 4. Since there are only 2 episodes where we find ourselves on State A, and each episode has equal proabibility to go from C to D and C to E, hence we will assign each of the episodes a weightage of 50% or 0.5. Next we simply add all the rewards for the respected episodes, multiple the total of each episode by its weightage to obtain the total rewards for that episode and then just add the total rewards for both episodes. Notice we can only go to State C from State A
To find \(\hat{V}^\pi(B)\) we first look at all the episodes to find instances where we are on State A. These instances can be found in episodes 2 and 3. Since there are only 2 episodes where we find ourselves on State B, and each episode has equal proabibility to go from C to D and C to E, hence we will assign each of the episodes a weightage of 50% or 0.5. Next we simply add all the rewards for the respected episodes, multiple the total of each episode by its weightage to obtain the total rewards for that episode and then just add the total rewards for both episodes. Notice we can only go to State C from State B


“A computer program is said to learn from experience E with respect to some task
T and some performance measure P, if its performance on T, as measured by P,
improves with experience E.”
- Tom Mitchell, 1997
Your spam filter is a Machine Learning program that, given samples of spam
emails (e.g., flagged by users) and samples of regular (non-spam, also called
“ham”) emails, can learn to flag spam. The examples that the system uses to find
out are called the training set. Each training example is named a training instance
(or sample). In this case, the task T is to flag spam for brand spanking new emails,
the experience E is that the training data, and therefore the performance measure P
must be defined; for example, you'll use the ratio of correctly classified emails.
This particular performance measure is named accuracy, and it's often utilized in
classification tasks.This was an example of a classification problem
Machine Learning systems are often classified consistent with the quantity
and sort of supervision they get during training. There are four major
categories: supervised learning, unsupervised learning, semi-supervised
learning, and Reinforcement Learning.
SUPERVISED LEARNING
In supervised learning, the training set you feed to the algorithm includes
the desired solutions, called labels. In the case of a spam filter the labels
are “spam” and “not spam”.
A typical supervised learning task is classification. An example of a
classification problem is "spam filter", which is actually classifying if a mail
is spam or not. Classification includes predicting discrete values.
Another typical task is to predict a target numeric value or continuous
values, such as the price of a car, given a set of features (mileage, age,
brand, etc.) called predictors. This sort of task is called regression. To train
the system, you would like to offer it many samples of cars, including both
their predictors and their labels i.e., their prices.
We use different algorithms to implement classification and regression.
Here are a number of the foremost important supervised learning
algorithms :
❖ K-Nearest Neighbors
❖ Linear Regression
❖ Logistic Regression using different kernel function
❖ Support Vector Machines (SVMs)
❖ Decision Trees and Random Forests Classifier or Regressors
❖ Neural networks using ANN (Artificial Neural Networks)
UNSUPERVISED LEARNING
In unsupervised learning, as you might guess, the training data is
unlabeled. The system tries to learn without a teacher.
The algorithm tries to derive knowledge from a general input without the
assistance of a group of pre-classified examples that are prompt to build
descriptive models. A typical example of the application of these algorithms
is found in search engines.
For example, say you have a lot of data about your blog’s visitors. You may
want to run a clustering algorithm to undertake to detect groups of
comparable visitors .At no point does one tell the algorithm which A visitor
belongs to: it finds those connections without your help. For example, it'd
notice that 40% of your visitors are males who love comic books and
usually read your blog within the evening, while 20% are young sci-fi lovers
who visit during the weekends. If you employ a hierarchical clustering
algorithm, it's going to also subdivide each group into smaller groups. This
may help you target your posts for each group.
Reinforcement learning
The algorithm is in a position to find out counting on the changes that occur
in the environment in which it is performed. In fact, since every action has
some effect on the environment concerned, the algorithm is driven by the
same feedback environment. Some of these algorithms are used in speech
or text recognition.
How to build a machine learning model ?
➢ Web Scraping data or gathering data from Kaggle or UCI repository.
➢ Reading the data using Pandas or Numpy
➢ Analysing data using data visualization and plots with the help of
awesome python libraries like Matplotlib and seaborn.
➢ Identifying correlation using a heatmap and features to use in
predictions.
➢ Creating the final features and target Dataframe.
➢ Anticipating the best models for the dataset and importing models
from Scikit-learn.
➢ Fitting the features into the model.
➢ Creating predictions.
➢ Analysing accuracy and loss.
➢ Checking for underfitting and overfitting.


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Hexus, please fix
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const Filter = require('./Filter');
const { arrayOrString, COMPARATOR } = require('../lib');
/** @const {Symbol}*/
const FILTER_KEY = Symbol('Filter')
/** Builder for {@link SiteFilter} */
class SiteFilterBuilder{
constructor(){
this[FILTER_KEY] = new Filter({});
}
/**
* What type of sites to search in (the default is any). Available Types:
* news
* blogs
* discussions
* Only news: site_type:news
* News & Blogs: (site_type:news OR site_type:blogs)
* @param {(String|Array)} t - formats type
* @return {this}
*/
type(t){
let type = arrayOrString(t);
console.log(this[FILTER_KEY].site_type, t, type);
this[FILTER_KEY].site_type = this[FILTER_KEY].site_type === undefined ? type : this[FILTER_KEY].site_type.concat(type);
return this;
}
/**
* Formats specific site query
* @param {(String|Array)} s - Limit the results to a specific site or sites
* @return {SiteFilterBuilder} this
*/
site(s){
let site = arrayOrString(s);
this[FILTER_KEY].site = this[FILTER_KEY].site === undefined ? site : this[FILTER_KEY].site.concat(site)
return this
}
/**
* Webhose.io uses heuristics to determine the country origin of a site, by taking into account the site's IP, TLD and language.
* Many times the country origin isn't conclusive so it isn't set, therefor filtering by country may result in much less data than when filtering by language.
* Return posts from sites from Hong Kong: thread.country:HK
* To get the full country code list, visit countrycode.org.
* @param {String}
* @return {this}
*/
country(c){ this[FILTER_KEY].thread.country = c; return this}
/**
* Limit the results to a specific site suffix
* Return posts from sites where their top level domain (TLD) ends with .fr:
* @param {String}
* @return {this}
*/
suffix(s){ this[FILTER_KEY].site_suffix = s; return this}
/**
* Filter sites based on the domain and optionally by their sub-domain
* Return posts from Yahoo answers: site_full:answers.yahoo.com
* @param {String}
* @return {this}
*/
full(f){ this[FILTER_KEY].site_full = f; return this}
/**
* Limit the results to posts originating from sites categorized as one (or more) of the following.
* Return posts from sites categorized as sports or games related: (site_category:sports OR site_category:games)
* @param {(String|Array)}
* @return {this}
*/
category(c){
let cat = arrayOrString(c);
this[FILTER_KEY].site_category = this[FILTER_KEY].site_category === undefined ? cat : this[FILTER_KEY].site_category.concat(cat);
return this
}
/**
* A rank that specifies how popular a domain is (by monthly traffic)
* Search for posts from the top 1,000 sites: domain_rank:<1000
* @param {Number}
* @param {@link COMPARATOR}
* @return {this}
*/
domainRank(d, comp=COMPARATOR.LT){ this[FILTER_KEY].domain_rank = `${comp}${d}`; return this}
/**
* @return {Filter}
*/
build(){ return this[FILTER_KEY];}
}
module.exports = SiteFilterBuilder;


After writing various bits and bobs for sports websites, including 'TheSPORTBible', I decided to push myself further by working for my local papers. I covered non-league football for over a year, which involved writing match reports and interviewing players/managers at the age of 17. When the football season ended, I spent my summer after my A-Levels approaching different firms for work placements. I gathered work experience at CNNLondon, Daily Mail and talkSPORT. All of which required me to come up with quick, insightful, creative and informative articles to tight deadlines. If that wasn't enough to whet my appetite, I am now undergoing an NCTJ-accredited Sports Journalism course at the University of Brighton.
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This sample demonstrates how to design an application that is compatible across different Android versions. Applications should degrade gracefully on older platform versions, dropping features or providing when the platform support needed by features or functionality isn't available.
In this case, the CrossCompatibility application shows how to use APIs that are not available in all Android versions and
still create a single .apk
that runs on all Android versions.
minSdkVersion
in the uses-sdk
tag.
For more information on how to make your applications cross-compatible, please check out the original blogpost here.


Data narratives are containers of information about computationally generated data. They have three major components:
Different narrative accounts can be generated, each for a separate audience with different content and details. Data narratives help make science more transparent, because they ensure that the resources are persistent (always accessible) and that the generation of the data is presented in a way that humans can easily use.


Class Controller
Application controller: - template methods: (necessary to call parent at method begin) - Init() - called after controller is created - session start - all internal variables initialized except view - PreDispatch() - called after Init, before every controller action - view initialization - internal actions: - AssetAction() - handling internal MvcCore http request to get assets from packed package - url proxy method, reading request param proxy method - view rendering or no-rendering management - http responses and redirects management - basic error responses rendering - request termination (to write and close session)
Direct known subclasses
MvcCore\Ext\Auth\Virtual\ControllerIndirect known subclasses
MvcCore\Ext\Auth\Controller
public
|
#
__construct(
Create new controller instance - always called from \MvcCore app instance before controller is dispatched. Never used in application controllers. |
public
|
|
public
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#
PreDispatch( )
Application pre render common action - always used in application controllers. This is best time to define any common properties or common view properties. |
public
string
|
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public
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public
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public
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public
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public
string
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public
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public
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public
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public
|
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public
|
|
public
|
|
public
string
|
#
Url( string $controllerActionOrRouteName = 'Index:Index', array $params = array() )
Generates url by: - 'Controller:Action' name and params array (for routes configuration when routes array has keys with 'Controller:Action' strings and routes has not controller name and action name defined inside) - route name and params array (route name is key in routes configuration array, should be any string but routes must have information about controller name and action name inside) Result address should have two forms: - nice rewrited url by routes configuration (for apps with .htaccess supporting url_rewrite and when first param is key in routes configuration array) - for all other cases is url form: index.php?controller=ctrlName&action=actionName (when first param is not founded in routes configuration array) |
public
string
|
|
public
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#
RenderError( string $exceptionMessage = '' )
Render controller action for error or error plain text response. |
public
|
|
public
|
|
public static
|
protected
|
$request
Request object - parsed uri, query params, app paths... |
|
protected
|
$response
Response object - headers and rendered body |
|
protected
string
|
$controller
Requested controller name - dashed |
#
''
|
protected
string
|
$action
Requested action name - dashed |
#
''
|
protected
boolean
|
$ajax
Boolean about ajax request |
#
FALSE
|
protected
|
$view
Class store object for view properties |
#
NULL
|
protected
string
|
$layout
Layout name to render html wrapper around rendered view |
#
'layout'
|
protected
boolean
|
$viewEnabled
Boolean about disabled or enabled view to render at last |
#
TRUE
|
protected static
string
|
$staticPath
Path to all static files - css, js, imgs and fonts |
#
'/static'
|
protected static
string
|
$tmpPath
Path to temporary directory with generated css and js files |
#
'/Var/Tmp'
|


Bajeti your number one budget assistance. Let Bajeti do it for you.
MORE ABOUT BAJETIBudgetting is one of the key aspects in finance management.At Bajeti we got you covered. Bajeti is an online budget assistance that helps with expense calculation.Bajeti is an interactive web application where where users get to key in their expected ammount to be spent and the expenses.The expenditure list is not restricted as you can add as expenses you want or need, then our application runs it for you and calculates the difference from the total expenditure and the expected ammount to be spent. When the diffenrence comes out with a negative value this means the expenditure is more than the expcted ammount to be spent this means the user as gone beyond/above the expected ammount to be spent and the user can delete or edit the expense section as to be at to not go beyond the expcted ammount to be spent.


UKCloud Limited (“UKC”) and Virtual Infrastructure Group Limited (“VIG”) (together “the Companies”) – in Compulsory Liquidation
On 25 October 2022, the Companies were placed into Liquidation with the Official Receiver appointed as Liquidator and J Robinson and A M Hudson simultaneously appointed as Special Managers to manage the liquidation process on behalf of the Official Receiver.
Further information regarding the Liquidations can be found here: https://www.gov.uk/government/news/virtual-infrastructure-group-limited-and-ukcloud-limited-information-for-creditors-and-interested-parties
Contact details:
For any general queries relating to the Liquidations please email<EMAIL_ADDRESS>For customer related queries please email<EMAIL_ADDRESS>For supplier related queries please email<EMAIL_ADDRESS>How to use NetworkPolicy objects to connect services between projects
Overview
In OpenShift v3.11 clusters deployed from mid-February 2019, the default SDN plugin has been changed from ovs-multitenant to ovs-networkpolicy. This gives you the ability to create NetworkPolicy objects which allow granular control over the flow of communication between the pods, services and projects inside your cluster.
By default, we create a NetworkPolicy object, named allow-from-same-and-privileged-namespaces
, in every project, including those created after the cluster is deployed. This object allows all pods to communicate with each other inside the same project and allows communication from all pods and services to the default and openshift-monitoring projects. This mirrors the default pod security provided with the ovs-multitenant plugin.
This document refers to namespaces and projects. A project is an OpenShift construct on top of a Kubernetes namespace. For the purpose of this guide they can be thought of as the same thing, however, the oc label
commands that are performed on a namespace must be applied to the namespace object, they will not work if you attempt to apply them to the project object.
Verifying the cluster SDN
In order to verify if the cluster has the ovs-networkpolicy SDN you can use the following command:
oc get clusternetwork
This will show redhat/openshift-ovs-networkpolicy under the PLUGIN NAME. If this shows ovs-multitenant instead and you wish to use NetworkPolicy objects you will need to raise a service request via the My Calls section of the UKCloud Portal to look at the options for switching SDN.
Note
You need to have cluster-reader or higher privilege in the cluster for this command to work.
Interacting with NetworkPolicy objects
You can interact with NetworkPolicy objects from the command line using the full name networkpolicy or short name netpol, for example:
oc get netpol
- List NetworkPolicy objects in the current project.
oc describe netpol <name>
- Provides more detailed information on the named NetworkPolicy object.
oc get netpol <name> -o yaml
- Provides yaml output of the named NetworkPolicy object. Useful to reference existing rules when creating new objects.
Example of connecting services from two different projects
NetworkPolicy objects are scoped at the project level. This means if you create a NetworkPolicy object using a template it will be created inside the project you're in and the rules will apply to pods within that project, with the exception of remote projects you may be referencing inside the rule. The object without any rules will look like the following:
apiVersion: extensions/v1beta1
kind: NetworkPolicy
metadata:
name: <desired_name_of_object>
namespace: <desired_project> (if not provided this will default to the project you are in when you run oc create)
spec:
Inside the spec key, you can pass in rules that determine what the NetworkPolicy object does. The first thing you need to consider is the pods you want to target. You can do this using the podSelector
key.
You can use this to match all pods in a project or alternatively to match all pods with a certain label. You can leave the key blank to match all pods in a project or you can match all pods with a certain label. In our example, we want to match pods with the label role=webserver
:
apiVersion: extensions/v1beta1
kind: NetworkPolicy
metadata:
name: webserver-egress
namespace: webserver
spec:
podSelector:
matchLabels:
role: webserver
We've matched the pods inside our webserver project that have the label role=webserver
. Now we want to allow these pods to connect to a service inside a project we've named database. To do this, we'll use a namespace selector, which evaluates projects based on a label. In this case, we're going to be looking for the namespace label database=true
. First, we need to ensure that this label is applied using the following command:
Note
The cluster administrator role is required to add labels to namespaces.
oc label namespace database database=true
Our example NetworkPolicy object now looks like the following:
apiVersion: extensions/v1beta1
kind: NetworkPolicy
metadata:
name: webserver-egress
namespace: webserver
spec:
podSelector:
matchLabels:
role: webserver
egress:
- to:
- namespaceSelector:
matchLabels:
database: "true"
We now have a template for a NetworkPolicy object that matches any pods labelled role=webserver
inside the webserver project and allows them to communicate out to any projects labelled database=true
. This is only half the work; we still need the ingress allowance inside the database project. Our example template for that object looks like the following:
apiVersion: extensions/v1beta1
kind: NetworkPolicy
metadata:
name: database-ingress
namespace: database
spec:
podSelector:
matchLabels:
role: database
ingress:
- from:
- namespaceSelector:
matchLabels:
webserver: "true"
This object is created in the database project and matches any pods in the project with the label role=database
and allows ingress traffic from any project labelled webserver=true
. To achieve our desired outcome, we need to label our webserver project as follows:
Note
The cluster administrator role is required to add labels to namespaces.
oc label namespace webserver webserver=true
In our example, we've saved our templates in files called netpol.yaml and netpol1.yaml. Use the following commands to create the objects, substituting in the relevant filename:
oc create -f netpol.yaml
oc create -f netpol1.yaml
Now we've created the objects, any pods inside the webserver project matching the label role=webserver
can communicate out to the service inside the database project exposed by pods matching the label role=database
.
Note
You can test connectivity by curling the desired target service from the terminal of a pod that has been allowed access to the service. You can find the terminal in the UI or use oc rsh
to enter the pod.
Further information
To get a full understanding of the capabilities and options of a NetworkPolicy object we recommend reading: https://kubernetes.io/docs/concepts/services-networking/network-policies
Documentation on efficient NetworkPolicy rules: https://docs.openshift.com/container-platform/3.11/admin_guide/managing_networking.html#admin-guide-networking-using-networkpolicy-efficiently
Feedback
If you find a problem with this article, click Improve this Doc to make the change yourself or raise an issue in GitHub. If you have an idea for how we could improve any of our services, send an email to feedback@ukcloud.com.


class: center, middle, inverse, title-slide # 4.4 — Factor Markets: Labor ## ECON 306 • Microeconomic Analysis • Spring 2021 ### Ryan Safner
Assistant Professor of Economics
<EMAIL_ADDRESS>ryansafner/microS21
microS21.classes.ryansafner.com
--- class: inverse # Outline ### [Labor Supply Decisions](#12) ### [Labor Market for Competitive Firm](#26) ### [Labor Market for a Monopoly](#41) ### [Monopsony Power](#45) ### [Monopoly Power in Labor Markets: Unions](#65) --- # Returning to Firms .pull-left[ - Recall a firm uses technology that buys inputs, transforms them, and sells output `$$q=f(k,l)$$` - We classified inputs into the .hi[factors of production]: land, labor, capital - We *assumed* fixed factor prices - show up in total cost `\(=wL+rK\)` - Where do they come from? .hi[Factor markets] ] .pull-right[ .center[ ![:scale 70%](../images/inputoutput.png) ] ] --- # Circular Flow .center[ ![:scale 60%](../images/circularflow.png) ] --- # Firms’ Payments to Factors are Income To Households .quitesmall[

  Income Type 	  Amount (2016) 	  Percent

  Salaries and wages 	  $7217 Bn 	  68.45%

  Taxable pensions and annuities 	  $694 Bn 	  6.58%

  Partnership and S corporation net income 	  $629 Bn 	  5.97%

  Capital gains less losses 	  $621 Bn 	  5.89%

  Business net income 	  $389 Bn 	  3.69%

  Taxable Social Security benefits 	  $286 Bn 	  2.71%

  Taxable IRA distributions 	  $258 Bn 	  2.45%

  Ordinary dividends 	  $254 Bn 	  2.41%

  Total rental and royalty net income 	  $98 Bn 	  0.93%

  Taxable interest 	  $97 Bn 	  0.92%

 ] .source[Source: [Tax Foundation, 2018](https://taxfoundation.org/sources-of-personal-income-2016/)] --- # Firms’ Payments to Factors are Income To Households .pull-left[ .center[ ![](../images/Sources-of-Personal-Income-2016-Update-1.png) ] ] .pull-right[ .center[ ![](../images/FF612-chart-2.png) ] ] .source[Source: [Tax Foundation, 2018](https://taxfoundation.org/sources-of-personal-income-2016/)] --- # Supply and Demand in Factor Markets .pull-left[ - The price of a factor is governed by the same market forces as output: - .hi-red[Supply of Factor]: willingness of factor owners to accept and sell/rent their services to firms - landowners, workers, capitalists, resource owners, suppliers - .hi-blue[Demand for Factor]: willingness of firms to pay for/hire factor services ] .pull-right[  ] --- # Factor Market Prices and Opportunity Costs .pull-left[ - .hi[Factor price represents **opportunity cost** of hiring a factor for an alternative use] - Firms not only pay for direct use of a factor, but also indirectly for *not using* it in an alternate process! ] .pull-right[ .center[ ![:scale 100%](../images/disappear.jpg) ] ] --- # Factor Market Prices and Opportunity Costs .pull-left[ - .green[**Example**]: a producer of hammers buys steel, pays (the opportunity cost) for "taking" the steel away from alternative uses ] .pull-right[ .center[ ![:scale 100%](../images/disappear.jpg) ] ] --- # Factor Market Prices and Opportunity Costs .pull-left[ - .green[**Example**]: e.g. salary for a skilled worker must be high enough to keep them at their current firm, and not be attracted to other firms/industries ] .pull-right[ .center[ ![:scale 100%](../images/disappear.jpg) ] ] --- # Example Factor Market: Labor Markets .pull-left[ - Empirically, about 70% of total cost of production comes from labor - We’ll focus just on the .hi[market for labor] as an example factor market - Can do the same for *any* factor market - (e.g. capital, land, materials, etc.) - Next class, we’ll focus on the particularities of .hi-purple[capital markets] ] .pull-right[ .center[ ![](../images/labormarket.jpg) ] ] --- class: inverse, center, middle # Labor Supply Decisions --- # Labor Supply Decisions .pull-left[ - The .red[Supply of Labor] comes from **individual decisions to work** - Labor is considered a .hi[disutility] (a .hi[bad]) - **Opportunity cost** of labor is .hi[leisure] - But, labor generates .hi-purple[income] for .hi-purple[consumption] (a good) - Tradeoff: if you work more, you get more income, but less leisure ] .pull-right[ .center[ ![:scale 50%](../images/labor2.png) ![:scale 50%](../images/freedom.jpg) ] ] --- # Modeling Labor Supply Decisions .pull-left[ .smallest[ - Easiest to apply our consumer choice model between two goods: `$$u(C,\ell)$$` - `\(C\)`: consumption - `\(\ell\)`: hours of leisure - Define amount of hours worked: `$$H = 24-\ell$$` - .red[Budget constraint]: market price of leisure: `\(-w\)` - set price of `\(c\)` to $1, a “numeraire” good - slope = `\(-\frac{w}{1}\)`, i.e. `\(-w\)` ] ] .pull-right[  ] --- # Modeling Labor Supply Decisions .pull-left[ - Person will .hi-purple[optimally choose] to supply `\(H^\star = 24 - \ell^\star\)` hours - Enjoy `\(\ell^\star\)` leisure and consume `\(c^\star\)` goods from labor income `\(wH^\star\)` ] .pull-right[  ] --- # Modeling Labor Supply Decisions: A Change In Wages .pull-left[ .smaller[ - What will happen to the optimal labor supply decision if wage `\(w\)` increases? - It depends! - Leisure is a normal good, but this makes labor “inferior” `$$H = 24 -\ell$$` - `\(\uparrow \ell \implies \downarrow H\)` - This is why .hi-purple[income and substitution effects] are important! (remember [all that stuff](/content/1.6-content)? 😰) ] ] .pull-right[ .center[ ![](../images/freemoney.jpg) ] ] --- # Modeling Labor Supply Decisions: A Change In Wages .pull-left[ - .purple[(Overall) Price effect]: `\(A \rightarrow C\)` - Higher wage `\(w\)` leads to less leisure `\(\ell\)` and therefore, more hours worked `\(H\)` - Upward sloping .red[labor supply curve] ] .pull-right[  ] --- # Modeling Labor Supply Decisions: A Change In Wages .pull-left[ - .orange[Substitution effect]: as wage `\(w\)` increases, the price of leisure `\(\ell\)` is increasing, so consume less leisure (normal good) - Thus, work more hours - Graphically: under higher wage `\(BC_2\)`, substitute more `\(c\)` for less `\(\ell\)` (more labor) holding utility constant - `\(A \rightarrow B\)`: more `\(c\)`, less `\(\ell\)` (more `\(H)\)` ] .pull-right[  ] --- # Modeling Labor Supply Decisions: A Change In Wages .pull-left[ - .green[Real income effect]: the higher wage makes you wealthier in real terms, so buy more of everything (including `\(\ell\)`, meaning **work _fewer_ hours!**) - `\(B \rightarrow C\)`: attain higher indifference curve `\(\color{green}{u_2}\)` - “Inferior” good: higher wages induce *more* leisure (and fewer labor hours) ] .pull-right[  ] --- # Modeling Labor Supply Decisions: A Change In Wages .pull-left[ - Income & substitution effects cut against each other - If .orange[Substitution effect] `\(>\)` .green[Income effect], then we get a positive .purple[price effect]: - **Increase in wages** causes .hi-purple[more work] (less leisure) - Matches our intuition, .red[upward-sloping labor supply curve] ] .pull-right[  ] --- # Modeling Labor Supply Decisions: A Change In Wages .pull-left[ - If .green[Income effect] > .orange[Substitution effect], leading to a negative .purple[price effect]: - **Increase in wages** causes .hi-purple[less work] (more leisure) - “Giffen-style” scenario, but **very plausible** for labor! (unlike consumer goods) - Intuition: imagine having an income target for a big purchase, and your salary increases ] .pull-right[  ] --- # Modeling Labor Supply Decisions: A Change In Wages .pull-left[ - We often see “backward-bending” labor supply curves - Depends on whether income or substitution effect dominates ] .pull-right[  ] --- # A Brief Digression on Economic Rents I .pull-left[ - Recall .hi-red[market supply] is the **minimum willingness to accept**, the minimum price necessary to bring a resource to market (its opportunity cost) - But all (equivalent) labor is paid the *market wage*, `\(w^*\)` determined by market labor supply and labor demand ] .pull-right[  ] --- # A Brief Digression on Economic Rents II .pull-left[ - Some workers would have accepted a job for less than `\(w^*\)` - These inframarginal workers earn .hi[economic rent] in excess of what is needed to bring them into the market (their opportunity cost) ] .pull-right[  ] --- # A Brief Digression on Economic Rents III .pull-left[ - Consider a factor (such as land) for which the supply is perfectly inelastic (e.g. a fixed supply) - Then the **entire value of the land is economic rent**! - .hi-purple[The *less* elastic the supply of a factor, the *more* economic rent it generates!] ] .pull-right[  ] --- class: inverse, center, middle # Labor Market for a Competitive Firm --- # Derived Demand in Factor Markets .pull-left[ - Demand for factors is a .hi-purple[“derived demand”]: - Firm only demands inputs to the extent they **contribute to producing sellable output** - Firm faces a .hi-purple[tradeoff] when **hiring more labor**, as more labor `\(\Delta L\)` creates: 1. .hi[Marginal Benefit]: Increases output and thus revenue 2. .hi[Marginal Cost]: Increases costs ] .pull-right[ .center[ ![](../images/firms.png) ] ] --- # Marginal Revenue Product (of Labor) - Hiring more labor increases new output (i.e. labor's .hi-purple[`\\(MP_L\\)`]) - [Recall](/content/2.2-content/): `\(MP_L=\frac{\Delta q}{\Delta L}\)`, where `\(q\)` is units of output -- - Additional output generates new revenues (i.e. labor's .hi-purple[`\\(MR(q)\\)`]) - Recall: `\(MR(q)=\frac{\Delta R(q)}{\Delta q}\)`, where `\(R(q)\)` is total revenue -- - Hiring more labor, on the **margin**, generates a **benefit**, called the .hi[marginal revenue product of labor, `\\(MRP_L\\)`]: `$$MRP_L=MP_L* MR(q)$$` - i.e. the number of new products a new worker makes times the revenue earned by selling the new products --- # Marginal Revenue Product for *Competitive* Firms .pull-left[ - This is the .hi-blue[Firm's Demand for Labor]: `$$MRP_L=MP_L* MR(q)$$` - For a firm in a .hi[competitive (output) market], firm's `\(MR(q)=p\)`, hence: `$$MRP_L=MP_L*p$$` where `\(p\)` is the price of the firm's *output* ]  --- # Marginal Revenue Product for *Competitive* Firms .pull-left[ `$$MRP_L=MP_L* p$$` - Marginal benefit of hiring labor, `\(MRP_L\)` **falls** with more labor used - production exhibits **diminishing marginal returns to labor**! - .hi[Choke price for labor demand]: price too high for firm to purchase any labor ]  --- # A Competitive *Factor* Market .pull-left[  ] .pull-right[  ] - If the .hi[*factor* market is competitive], labor supply for an individual firm is *perfectly elastic* at the market price of labor `\((w^*)\)` --- # Labor Supply and Firm's Demand for Labor .pull-left[ - We've seen a falling `\(MRP_L\)`, the marginal benefit of hiring labor - .hi[Marginal cost of hiring labor], `\(w\)`, remains constant - so long as firm is not a big purchaser (has no market power) in the labor market ] .pull-right[  ] --- # Labor Supply and Firm's Demand for Labor .pull-left[ - At low amounts of labor, .blue[marginal benefit] `\(\color{#0047AB}{MRP_L} > \color{#D7250E}{w}\)` .red[marginal cost] - Firm will hire more labor ] .pull-right[  ] --- # Labor Supply and Firm's Demand for Labor .pull-left[ - At high amounts of labor, .blue[marginal benefit] `\(\color{#0047AB}{MRP_L} < \color{#D7250E}{w}\)` .red[marginal cost] - Firm will hire less labor ] .pull-right[  ] --- # Labor Supply and Firm's Demand for Labor .pull-left[ - Firm hires `\(L^*\)` optimal amount of labor where `\(w=MRP_L\)` - i.e. .red[marginal cost] of labor `\(=\)` .blue[marginal benefit] of labor ] .pull-right[  ] --- # Labor Supply and Firm's Demand for Labor .pull-left[  ] .pull-right[  ] --- # Labor Supply and Firm's Demand for Labor .pull-left[  ] .pull-right[  ] .smaller[ - If market supply of labor decreases, wages increase & firms hire fewer workers (and vice versa) ] --- # Example .bg-washed-green.b--dark-green.ba.bw2.br3.shadow-5.ph4.mt5[ .green[**Example**]: Victoria’s Tours is a travel company that offers guided tours of nearby mountain biking trails. Its marginal revenue product of labor is given by `\(MRP_L = 1,000 – 40l\)`, where `\(l\)` is the number of tour-guide weeks it hires and `\(MRP_L\)` is measured in dollars per tour-guide week. The going market wage for Victoria’s Tours is $600 per tour-guide week. ] 1. What is the optimal amount of labor for Victoria’s Tours to hire? 2. At and above what market wage would Victoria’s Tours not want to hire anyone? 3. What is the most labor Victoria’s Tours would ever hire, given its marginal revenue product? --- class: inverse, center, middle # Labor Demand for a Monopoly --- # Labor Demand for Competitive vs. Monopolist Firm .pull-left[  ] .pull-right[ - Recall a firm's demand for labor: `\(MRP_L= MP_L * MR(q)\)` - A firm in a **competitive output industry** has its `\(MR(q)=p\)` - So we saw its .hi-blue[Labor Demand], `\(MRP_L = MP_L * p\)` ] --- # Labor Demand for Competitive vs. Monopolist Firm .pull-left[  ] .pull-right[ - Recall if firm is a **monopolist** in its **output** industry, its `\(MR(q) < p\)` - So its .hi-blue[Labor Demand], `\(MRP_L = MRP_L * MR(q)\)` - Since `\(MR(q) < p\)`, .hi-purple[a monopoly in its output industry will always have lower demand for labor], and thus, .hi-purple[hire less labor than a competitive firm] - Monopoly produces less output, so wants fewer inputs! ] --- # Labor Demand for Competitive vs. Monopolist Firm .pull-left[  ] .pull-right[ - This is about the competitiveness of the **output** or .hi-purple["downstream"] market - Here, both competitive firm and monopolist in downstream markets face the same perfectly elastic .hi-red[labor supply] - We've assumed no market power in the **input** or .hi-purple["upstream"] market (for labor) - We next consider market power in the upstream (labor) market... ] --- class: inverse, center, middle # Monopsony Power --- # Monopsony .pull-left[ - What if the firm has .hi-purple[market power in a factor market]? - Consider extreme example: .hi[monopsony]: a factor market with a **single buyer** ] .pull-right[ .center[ ![](../images/monopolyman.jpg) ] ] --- # Monopsony and Market Supply of Labor .pull-left[  ] .pull-right[ - Market power in *hiring* labor implies that the firm faces the **whole market factor supply curve** for labor - Market supply is upward sloping - .hi-red[Factor (inverse) supply] describes minimum price workers are willing to accept to work ] --- # Monopsony and Market Supply of Labor .pull-left[  ] .pull-right[ - As firm chooses to hire more `\(L\)`, must raise wages on *all* workers to hire them ] --- # Monopsony and Market Supply of Labor .pull-left[  ] .pull-right[ - As firm chooses to hire more `\(L\)`, must raise wages on *all* workers to hire them - .green[Output effect]: increased cost from increased number of workers ] --- # Monopsony and Market Supply of Labor .pull-left[  ] .pull-right[ - As firm chooses to hire more `\(L\)`, must raise wages on *all* workers to hire them - .green[Output effect]: increased cost from increased number of workers - .red[Price effect]: increased cost from raising wage for all workers ] --- # Monopsony and Marginal Cost of Labor I - If monopsonist wants to hire more labor, `\(\Delta L\)`, its labor cost `\(C(L)\)` would change by: .center[ `\(\Delta C(L)=\)`.green[`\\(w \Delta L\\)`] `\(+\)` .red[`\\(L \Delta w\\)`] ] -- - .green[Output effect]: increases number of labor hired `\((\Delta L)\)` times wage `\(w\)` per worker -- - .red[Price effect]: raises wage per worker `\((\Delta w)\)` on *all* workers hired `\((L)\)` -- - Divide both sides by `\(\Delta L\)` to get .hi-purple[Marginal Cost of Labor, `\\(MC(L)\\)`]: `$$\frac{\Delta C(L)}{\Delta L}=\color{#6A5ACD}{MC(L)=w+\frac{\Delta w}{\Delta L}L}$$` -- .small[ - Compare: supply for a **price-taking** firm is perfectly elastic: `\(\color{#6A5ACD}{\frac{\Delta w}{\Delta L}}=0\)`, so we saw `\(\color{#6A5ACD}{MC(L)=w}\)`! ] --- # Monopsony and Marginal Cost of Labor II .smallest[ - If we have a linear inverse supply function for labor of the form `$$\color{#e64173}{w=a+bL}$$` - `\(a\)` is the choke price (intercept) - `\(b\)` is the slope ] -- .smallest[ - Marginal cost of labor again is defined as: `$$MC(L)=w+\color{#44C1C4}{\frac{\Delta w}{\Delta L}}L$$` ] -- .smallest[ - Recognize that `\(\color{#44C1C4}{\frac{\Delta w}{\Delta L}}\)` is the slope, `\(b\)`, `\(\left(\frac{rise}{run} \right)\)` ] -- .smallest[ `$$\begin{align*} MC(L)&=w+(\color{#44C1C4}{b})L\\ MC(L)&=(\color{#e64173}{a+bL})+bL\\ \color{#6A5ACD}{MC(L)}&=\color{#6A5ACD}{a+2bL}\\ \end{align*}$$` ] --- # Monopsony and Marginal Cost of Labor IV .pull-left[  ] .pull-right[ `$$\begin{align*} \color{#D7250E}{w(L)}&\color{#D7250E}{=a+bL}\\ \color{#8b1a1a}{MC(L)}&\color{#8b1a1a}{=a+2bL}\\ \end{align*}$$` - Marginal cost of labor starts at same intercept as Supply (average cost of labor) `\((a)\)` with twice the slope `\((2b)\)` .tiny[ Note: If these past few slides have sounded familiar, this is the [exact same process](/slides/4.1-slides#14) by which we derived a *monopolist*’s marginal *revenue* curve! ] ] --- # Monopsony's Hiring Decisions .pull-left[  ] .pull-right[ - Optimal quantity is where `\(MC=MR\)` - Firm's `\(MC(L)=MRP_L\)` ] --- # Monopsony's Hiring Decisions .pull-left[  ] .pull-right[ - Optimal quantity is where `\(MC=MR\)` - Firm's `\(MC(L)=MRP_L\)` - Monopsonist faces *entire* .hi-red[market supply] - Can lower wages as low as workers’ minimum WTA (Supply) ] --- # Monopsonist’s Hiring Decisions .pull-left[  ] .pull-right[ - Optimal quantity is where `\(MC=MR\)` - Firm's `\(MC(L)=MRP_L\)` - Monopsonist faces *entire* .hi-red[market supply] - Can lower wages as low as workers' minimum WTA (Supply) - Compared to a competitive labor market `\((L_c,w_c)\)`, .hi-purple[monopsonist hires fewer workers and pays them lower wages] `\((L_m,w_m)\)` - Creates **deadweight loss** ] --- # Monopsony Depends on Price Elasticity of Supply .center[ .smallest[ .hi-purple[he more (less) elastic labor supply, the less (more) monopsony power (and DWL)] ] ] .pull-left[ .center[ .smallest[ *Less* Elastic Labor Supply Curve ] ]  ] .pull-right[ .center[ .smallest[ *More* Elastic Labor Supply Curve ] ]  ] --- class: inverse, center, middle # Monopoly Power in Labor Markets: Unions --- # Monopoly Power in Labor Markets: Unions .pull-left[  ] .pull-right[ - If **seller**/s of labor (workers) has market power, can act like a **monopolist** on the labor market - .hi-green[Example]: A labor union - Faces entire market demand for labor, and thus its marginal revenue curve too - Acts like a monopolist, restricts `\(L_u < L_c\)` to push up `\(w_u > w_c\)` ] --- # The Problem of Bilateral Monopoly .pull-left[ - What if **both** sides of the market **have market power**? - A downstream .hi-blue[monopsonist buyer] vs. an upstream .hi-red[monopolist seller] - This is the problem of .hi[bilateral monopoly] - Find out more in my [industrial organization course](https://ios20.classes.ryansafner.com) - One solution is .hi-purple[vertical integration]: merge into a single firm across both markets ] .pull-right[ .center[ ![](../images/negotiation.jpg) ] ]