Need the rest of this code in Python- import math import pandas as pd.pdf
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Need the rest of this code in Python: import math import pandas as pd def information_gain_target(dataset_file): # Input: dataset_file - A string variable which references the path to the dataset file. # Output: ig_loan - A floating point variable which holds the information gain associated with the target variable. # # NOTE: # 1. Return the information gain associated with the target variable in the dataset. # 2. The Loan attribute is the target variable # 3. The pandas dataframe has the following attributes: Age, Income, Student, Credit Rating, Loan # 4. Perform your calculations for information gain and assign it to the variable ig_loan df = pd.read_csv(dataset_file) ig_loan = 0 # your code here.
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#include <functional> // std::less
#include <iostream>
#include <queue> // std::queue
#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
{
public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
: element{ theElement }, left{ lt }, right{ rt } { }
BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
}
BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
const value_type & find( const key_type & key ) const { return find( key, _root )-
>element.second; }
bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
clear( _root );
_size = 0;
}
void insert( const_reference x ) { insert( x, _root ); }
void insert( pair && x ) { insert( std::move( x ), _root ); }
void erase( const key_type & x ) { erase(x, _root); }
BinarySearchTree & operator=( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree & operator=( BinarySearchTree && rhs ) {
// TODO
}
private:
void insert( const_reference x, node_ptr & t ) {
// TODO
}
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// TODO
}
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// TODO
}
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// TODO
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// TODO
}
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// TODO
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// TODO
}
const_node_ptr find( const key_type & key, const_node_ptr t ) const {
// TODO
}
void clear( node_ptr & t ) {
// TODO
}
node_ptr clone ( const_node_ptr t ) const {
// TODO
}
public:
template <typename KK, typename VV, typename CC>
friend void printLevelByLevel( const BinarySearchTree<KK, VV, CC>& bst, std::ostream & out
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import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
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model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
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#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
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model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
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model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
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#include<stdlib.h>
#include<math.h>
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#define MAX_S 10
struct tugboat {
int x, y, dist;
char name[21];
};
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const struct tugboat* t1 = (const struct tugboat*) a;
const struct tugboat* t2 = (const struct tugboat*) b;
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}
int main() {
int xs, ys, xn, yn;
scanf("%d%d%d%d", &xs, &ys, &xn, &yn);
int n;
scanf("%d", &n);
struct tugboat tugboats[MAX_N];
int i;
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scanf("%d%d%s", &tugboats[i].x, &tugboats[i].y, tugboats[i].name);
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int dy = tugboats[i].y - ys;
tugboats[i].dist = dx * dx + dy * dy;
}
qsort(tugboats, n, sizeof(struct tugboat), cmp);
int num_tugs = 0;
char tugs[MAX_N][21];
int prev_x = xs, prev_y = ys;
for (i = 0; i < n; i++) {
if (tugboats[i].x >= prev_x && tugboats[i].y >= prev_y) {
num_tugs++;
strcpy(tugs[num_tugs - 1], tugboats[i].name);
prev_x = tugboats[i].x;
prev_y = tugboats[i].y;
}
}
printf("%d\n", num_tugs);
for (i = 0; i < num_tugs; i++) {
printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
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object based on its id value (user supplies id and should display rental fee value to the user) -
selection to exit program 4) Program should throw and catch Java built-in and user-defined
exceptions as appropriate 5) Your classes must be coded with correct encapsulation:
private/protected attributes, get methods, and set methods and value validation 6) There should
be appropriate polymorphism: overloading, overriding methods, and dynamic binding 7)
Program should take advantage of the inheritance properties as appropriate.
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#pragma once
#include <functional> // std::less
#include <iostream>
#include <queue> // std::queue
#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
{
public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
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BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
}
BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
const value_type & find( const key_type & key ) const { return find( key, _root )-
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bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
clear( _root );
_size = 0;
}
void insert( const_reference x ) { insert( x, _root ); }
void insert( pair && x ) { insert( std::move( x ), _root ); }
void erase( const key_type & x ) { erase(x, _root); }
BinarySearchTree & operator=( const BinarySearchTree & rhs ) {
// TODO
}
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// TODO
}
private:
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// TODO
}
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// TODO
}
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// TODO
}
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// TODO
}
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// TODO
}
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// TODO
}
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// TODO
}
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// TODO
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// TODO
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import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
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#Selecting a single class of images
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model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
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model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
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model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
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struct tugboat {
int x, y, dist;
char name[21];
};
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printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
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This Presentation in details discusses on Digital Tools and AI for Teaching Learning and Research
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This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
How to Make a Field invisible in Odoo 17
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Embracing GenAI - A Strategic Imperative
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
How libraries can support authors with open access requirements for UKRI fund...
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Unit 2- Research Aptitude (UGC NET Paper I).pdf
This slide describes the research aptitude of unit 2 in the UGC NET paper I.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Honest Reviews of Tim Han LMA Course Program.pptx
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
The Challenger.pdf DNHS Official Publication
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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Need the rest of this code in Python- import math import pandas as pd.pdf
- 1. Need the rest of this code in Python: import math import pandas as pd def information_gain_target(dataset_file): # Input: dataset_file - A string variable which references the path to the dataset file. # Output: ig_loan - A floating point variable which holds the information gain associated with the target variable. # # NOTE: # 1. Return the information gain associated with the target variable in the dataset. # 2. The Loan attribute is the target variable # 3. The pandas dataframe has the following attributes: Age, Income, Student, Credit Rating, Loan # 4. Perform your calculations for information gain and assign it to the variable ig_loan df = pd.read_csv(dataset_file) ig_loan = 0 # your code here