- هل تيمورلنك رجل عظيم كما يدّعي أقوام ما وراء النهر ومصلح للعالم الإسلامي!. أم هو قاهر العالم بالطغيان والبغي والقتل والتحريق!...
- كيف يذكره أقوام ما وراء النهر ويصلّون عليه كما يصلّون على الأنبياء، وفي البلاد العربية فهو مذموم على كلّ لسان!
- لماذا كان يفضّل سلالة رسول الله صلى الله عليه وسلم من أهل البيت ويبجّلهم تبجيلاً، ولماذا كان يبجّل أهل العلم والدين ويعظّمهم تعظيماً!...
- هل هو شيعيّ المذهب أم سنّي، أم أنّه كان ينصر الحقّ في أيّ مذهب وجده؟...
كيف كان الفاتح الذي لم يقهر؟.
- رجل بمفرده وُزنت مقدرته فتفوّقت على أمم الأرض، انتصر على أكاسرة وقياصرة الكوكب الأرضي ولم ينكسر في معركة أبداً. فمن هو هذا الرجل وما حقيقته؟..
- هل تيمورلنك رجل عظيم كما يدّعي أقوام ما وراء النهر ومصلح للعالم الإسلامي!. أم هو قاهر العالم بالطغيان والبغي والقتل والتحريق!...
- كيف يذكره أقوام ما وراء النهر ويصلّون عليه كما يصلّون على الأنبياء، وفي البلاد العربية فهو مذموم على كلّ لسان!
- لماذا كان يفضّل سلالة رسول الله صلى الله عليه وسلم من أهل البيت ويبجّلهم تبجيلاً، ولماذا كان يبجّل أهل العلم والدين ويعظّمهم تعظيماً!...
- هل هو شيعيّ المذهب أم سنّي، أم أنّه كان ينصر الحقّ في أيّ مذهب وجده؟...
كيف كان الفاتح الذي لم يقهر؟.
- رجل بمفرده وُزنت مقدرته فتفوّقت على أمم الأرض، انتصر على أكاسرة وقياصرة الكوكب الأرضي ولم ينكسر في معركة أبداً. فمن هو هذا الرجل وما حقيقته؟..
Lataif e ashrafi malfoozat e syed makhdoom ashraf 25Aale Rasool Ahmad
لطائف اشرفی حصہ اول
یا
ملفوظات سید مخدوم اشرف جہانگیرسمنانی کچھوچھوی
مترجم
حضرت شمس بریلوی
نظر ثانی
ڈاکٹر خضر نوشاہی
مدیر و ناشر
نذر اشرف شیخ محمد ہاشم رضا اشرفی
خلیفہ مجاز مخدوم المشائخ غوث الوقت سید مختار اشرف اشرف جیلانی سرکارکلاں کچھوچھو شریف
یہ کتاب تارک السلطنت غوث العالم محبوب یزدانی سلطان اوحدالدین قدوۃ الکبریٰ مخدوم سیداشرف جہانیاں جہانگیر سمنانی رضی اللہ تعالیٰ عنہ کے ملفوظات اور ارشادات و کمالات و فضائل پر مبنی ہے جسے انکے مرید خاص ابوالفضائل شیخ الاسلام والمسلمین حضرت نظام یمنی المعروف نظام حاجی غریب یمنی قدس سرہ النورانی نے مرتب کیا ۔ وہ آپکے خدمت میں تیس سال رہے۔
اپلود آن ارچیو
دیوانۂ مجمع البحرین حاجی الحرمین الشریفین اعلیٰ حضرت قدسی منزلت مخدوم الاولیاء مرشدالعالم محبوب ربانی ہم شبیہ غوث الاعظم حضرت سید شاہ ابواحمد المدعومحمد علی حسین اشرف اشرؔفی میاں الحسنی الحسینی قدس سرہ النوران
الحاج شیخ آلِ رسول احمد الصدیقی الاشرفی القادری کٹیہاری
The Qur’aan is the Book of Allah subhaanahu wa ta‘aalaa. Every word in the Qur’aan has come from Allah. That is why we say that it is a Holy Book. The words in the Qur’aan were sent by Allah to Prophet Muhammad sallal-laahu ‘alayhi wa-aalihi wa sallam. The Prophet (s) received the words of Allah through angel Jibra’eel. This Qur’aan is a Holy Book that was not written by anyone but sent by Allah to Prophet Muhammad (s) through Jibra’eel.
The document describes the Apriori algorithm for frequent itemset mining and association rule learning. Apriori uses a bottom-up approach where frequent subsets are extended one item at a time, and groups of candidates are tested against the data. This allows pruning of itemsets that are not frequent, reducing computational time. The algorithm proceeds in multiple passes over the transaction data set, where itemsets found to be frequent in the first pass are extended one item per pass.
The document describes the support vector machine (SVM) algorithm for classification. It discusses how SVM finds the optimal separating hyperplane between two classes by maximizing the margin between them. It introduces the concepts of support vectors, Lagrange multipliers, and kernels. The sequential minimal optimization (SMO) algorithm is also summarized, which breaks the quadratic optimization problem of SVM training into smaller subproblems to optimize two Lagrange multipliers at a time.
The document discusses the history and use of structural aluminum profile in machine building and manufacturing. It began in Europe in the 1970s as a lighter alternative to welded steel that offered more flexibility. While initially used mostly for basic applications in North America, many companies have adopted it for more complex automated processes due to its strength, reuseability, and lower total cost compared to steel. The future of structural aluminum profile in North America is promising as the market grows for customized, modular solutions that can be engineered and assembled more quickly.
This document provides instructions for setting up an interactive notebook for an Introduction to Biology class. Students should have their course information sheet signature page ready to hand in. They should also get out 7 pages of binder paper and the assignment sheet. The cover of the notebook should include the class name, student name and information, and a related picture. The pages should be numbered and have designated sections for warm-ups, homework, input, and output. The input section provides an introduction to biology, defining it as the study of living things, and outlines some of the major topics that will be covered, including diversity of life, cell biology, genetics, evolution, and ecology. Students are instructed to look further into one interesting topic and illustrate
La sociedad de la información se caracteriza por el uso generalizado de las nuevas tecnologías de la información y la comunicación (TIC), lo que lleva a la mundialización y nuevos valores culturales. La información es cada vez más audiovisual, multimedia e hipertextual debido a su almacenamiento y procesamiento digital. Jacques Delors identifica cuatro ámbitos clave de aprendizaje: ser, saber, hacer y convivir. Como futura docente, la autora debería reforzar más el ámbito de "saber" para luego implementarlo en el aula
Lataif e ashrafi malfoozat e syed makhdoom ashraf 25Aale Rasool Ahmad
لطائف اشرفی حصہ اول
یا
ملفوظات سید مخدوم اشرف جہانگیرسمنانی کچھوچھوی
مترجم
حضرت شمس بریلوی
نظر ثانی
ڈاکٹر خضر نوشاہی
مدیر و ناشر
نذر اشرف شیخ محمد ہاشم رضا اشرفی
خلیفہ مجاز مخدوم المشائخ غوث الوقت سید مختار اشرف اشرف جیلانی سرکارکلاں کچھوچھو شریف
یہ کتاب تارک السلطنت غوث العالم محبوب یزدانی سلطان اوحدالدین قدوۃ الکبریٰ مخدوم سیداشرف جہانیاں جہانگیر سمنانی رضی اللہ تعالیٰ عنہ کے ملفوظات اور ارشادات و کمالات و فضائل پر مبنی ہے جسے انکے مرید خاص ابوالفضائل شیخ الاسلام والمسلمین حضرت نظام یمنی المعروف نظام حاجی غریب یمنی قدس سرہ النورانی نے مرتب کیا ۔ وہ آپکے خدمت میں تیس سال رہے۔
اپلود آن ارچیو
دیوانۂ مجمع البحرین حاجی الحرمین الشریفین اعلیٰ حضرت قدسی منزلت مخدوم الاولیاء مرشدالعالم محبوب ربانی ہم شبیہ غوث الاعظم حضرت سید شاہ ابواحمد المدعومحمد علی حسین اشرف اشرؔفی میاں الحسنی الحسینی قدس سرہ النوران
الحاج شیخ آلِ رسول احمد الصدیقی الاشرفی القادری کٹیہاری
The Qur’aan is the Book of Allah subhaanahu wa ta‘aalaa. Every word in the Qur’aan has come from Allah. That is why we say that it is a Holy Book. The words in the Qur’aan were sent by Allah to Prophet Muhammad sallal-laahu ‘alayhi wa-aalihi wa sallam. The Prophet (s) received the words of Allah through angel Jibra’eel. This Qur’aan is a Holy Book that was not written by anyone but sent by Allah to Prophet Muhammad (s) through Jibra’eel.
The document describes the Apriori algorithm for frequent itemset mining and association rule learning. Apriori uses a bottom-up approach where frequent subsets are extended one item at a time, and groups of candidates are tested against the data. This allows pruning of itemsets that are not frequent, reducing computational time. The algorithm proceeds in multiple passes over the transaction data set, where itemsets found to be frequent in the first pass are extended one item per pass.
The document describes the support vector machine (SVM) algorithm for classification. It discusses how SVM finds the optimal separating hyperplane between two classes by maximizing the margin between them. It introduces the concepts of support vectors, Lagrange multipliers, and kernels. The sequential minimal optimization (SMO) algorithm is also summarized, which breaks the quadratic optimization problem of SVM training into smaller subproblems to optimize two Lagrange multipliers at a time.
The document discusses the history and use of structural aluminum profile in machine building and manufacturing. It began in Europe in the 1970s as a lighter alternative to welded steel that offered more flexibility. While initially used mostly for basic applications in North America, many companies have adopted it for more complex automated processes due to its strength, reuseability, and lower total cost compared to steel. The future of structural aluminum profile in North America is promising as the market grows for customized, modular solutions that can be engineered and assembled more quickly.
This document provides instructions for setting up an interactive notebook for an Introduction to Biology class. Students should have their course information sheet signature page ready to hand in. They should also get out 7 pages of binder paper and the assignment sheet. The cover of the notebook should include the class name, student name and information, and a related picture. The pages should be numbered and have designated sections for warm-ups, homework, input, and output. The input section provides an introduction to biology, defining it as the study of living things, and outlines some of the major topics that will be covered, including diversity of life, cell biology, genetics, evolution, and ecology. Students are instructed to look further into one interesting topic and illustrate
La sociedad de la información se caracteriza por el uso generalizado de las nuevas tecnologías de la información y la comunicación (TIC), lo que lleva a la mundialización y nuevos valores culturales. La información es cada vez más audiovisual, multimedia e hipertextual debido a su almacenamiento y procesamiento digital. Jacques Delors identifica cuatro ámbitos clave de aprendizaje: ser, saber, hacer y convivir. Como futura docente, la autora debería reforzar más el ámbito de "saber" para luego implementarlo en el aula
SWAN 2015 Conference D McCombie 29 April 2015 pm session FINALDuncan McCombie
Smart meters and data collection can help provide tailored water and energy use advice to households. By analyzing usage data from large numbers of smart meters, providers can better understand household water and energy behaviors. This data can then be combined with technical insights to give individual households personalized advice on how to reduce consumption in ways that fit their lifestyles and circumstances. Translating usage data into meaningful, easy-to-understand information and recommendations helps households make informed decisions to lower their bills while maintaining comfort.
El documento habla sobre RSS (Really Simple Syndication), que es una forma sencilla de recibir información actualizada de páginas web favoritas de manera automática sin necesidad de visitarlas individualmente. Para hacer uso de RSS, se requiere un lector RSS que puede ser un programa instalado, el navegador web, un programa de correo o un lector en línea. Una vez configurado el lector RSS, el usuario puede suscribirse a las páginas que desee seguir y tendrá todas sus actualizaciones en un solo lugar de manera automática y ahorrand
Poonam Thakur is seeking a position as a market research analyst. She has over 3 years of experience in this role at Sarens Heavy Lift India Pvt. Ltd., where she conducted market research, created databases and company profiles, presented findings to management, and more. Prior to this, she worked as a finance executive. Poonam has an MBA in finance and marketing and skills in Microsoft Office, data analysis, and communication. She is motivated to learn and grow in a role supporting company growth through market research.
Nuclear reactions release a lot of energy. There are two main types of nuclear reactions: nuclear fission and nuclear fusion. Nuclear fission occurs when large atomic nuclei split into smaller pieces, becoming more disorganized. It is a spontaneous process that can trigger chain reactions, such as when uranium-235 absorbs a neutron. Nuclear fusion occurs when smaller atomic nuclei combine to form larger nuclei, becoming more organized. It requires high temperatures and is not a spontaneous process.
El documento trata sobre los procesos vitales básicos como la evolución en general y la evolución de los homínidos. Explica las cuatro grandes ramas de familias en la historia humana como los grupos Ardipithecus, Australopithecus, Paranthropus y el Homo naledi. También discute sobre el ADN, la evolución del aprendizaje humano, la educabilidad y el desarrollo del cerebro a través del aprendizaje y la resolución de problemas.
Scientists design controlled experiments to test hypotheses and answer testable questions about the natural world. They identify an independent variable to manipulate and keep all other variables constant. For example, an experiment could test if fertilizer increases plant growth by applying fertilizer to half the plots and controlling variables like soil type, water, and sunlight. Good experiments are replicated multiple times to improve reliability. Observations can be quantitative by including numbers or qualitative with descriptions. The results are analyzed to determine if the independent variable affected the outcome.
Este documento clasifica y define diferentes tipos de información gráfica como diagramas, gráficas, mapas, ilustraciones y lenguaje visual. Explica que las gráficas expresan relaciones cuantitativas a través de tablas de datos y gráficas de barras. Los diagramas expresan relaciones conceptuales mediante esquemas y mapas conceptuales. Los mapas, planos y croquis representan objetos en un plano, mientras que las ilustraciones expresan relaciones espaciales a través de dibujos y fotografías.
The document provides an introduction to the R programming language. It discusses downloading and installing R, using basic functions and operations in R like vectors, matrices, conditional statements, loops, and applying functions. It also lists some online resources for learning more about R including R tips and RjpWiki websites that provide tutorials and documentation on using R.
1. The document discusses molecular biology concepts including DNA, mutations, molecular evolution, and phylogenetic analysis methods.
2. It provides examples of different types of DNA mutations like transitions, transversions, synonymous and nonsynonymous substitutions.
3. Common phylogenetic analysis methods are described briefly, including Neighbor-Joining, Maximum Parsimony, and Maximum Likelihood. Distances between DNA sequences are represented in examples.
This document discusses analyzing and classifying iris flower data using decision trees. It loads iris training and test data, builds a decision tree classifier using rpart that achieves 96.7% accuracy on the test data, and visualizes the tree and iris measurements. Key steps include loading data, building a decision tree with maximum depth of 2 nodes, plotting the tree and iris measurements, and evaluating accuracy on test data.
HP Distributed R is a high-performance scalable platform for the R language. It enables R to
leverage multiple cores and multiple servers to perform Big Data Advanced Analytics. It consists of
new R language constructs to easily parallelize algorithms across multiple R processes.
HP Distributed R simplifies large-scale analysis by extending R. Because R is a single-threaded
environment, it has limited utility for Big Data analytics. HP Distributed R allows you to specify that
parts of programs be run in multiple single-threaded R-processes. This approach results in
significantly reduced execution times for Big Data analysis.
The document contains references to multiple figures and tables across several pages. Figures 1, 2, 3, 4, and 5 are referenced, along with Table 1. The figures are cited in groups or individually with labels a through j.
The document discusses a lecture on next generation sequencing analysis for model and non-model organisms. It covers topics like RNA-Seq analysis, genome and RNA assembly, and introduction to the AWK programming language. The lecture also includes exercises on visualizing mapped reads, performing RNA-Seq analysis, and genome assembly. Mapping, assembly, and visualization of reads from Arabidopsis thaliana and A. lyrata are discussed.
Next generation sequencing techniques were discussed including an overview of various sequencing platforms, their output, and common analysis workflows. Mapping short reads to reference genomes using alignment programs is a key first step for most applications. Formats like FASTQ, SAM, and BAM are commonly used to store sequencing reads and mapping results.
The document summarizes two papers presented at NIPS 2010:
1) "b-Bit Minwise Hashing for Estimating Three-Way Similarities" which introduces a method called b-bit minwise hashing to estimate Jaccard similarity between three sets using only b bits per element.
2) "Functional Geometry Alignment and Localization of Brain Areas" which presents a method called functional geometry alignment to register brain images based on functional data like fMRI rather than just anatomical data. It uses diffusion maps to embed voxel activities in a low-dimensional space and aligns these functional embeddings for registration.
This document describes the Apriori algorithm for frequent itemset mining. The Apriori algorithm uses a "bottom-up" approach, where frequent subsets are extended one item at a time to generate larger itemsets. To reduce the number of candidate itemsets, the algorithm prunes any itemset whose subset is not frequent. It performs multiple passes over the transaction database and uses a hash-tree structure to count candidate itemsets efficiently.
The document describes and compares different hierarchical clustering algorithms:
1) Single-link clustering connects clusters based on the closest pair of patterns, forming elongated clusters. Complete-link connects based on the furthest pair, forming more compact clusters.
2) Complete-link is more useful than single-link for most applications as it produces more interpretable hierarchies. However, single-link can extract certain cluster types that complete-link cannot, like concentric clusters.
3) Average group linkage connects clusters based on the average distance between all pairs of patterns in the two clusters. It provides a balance between single and complete link.
1. The document discusses classification algorithms on two datasets: IRIS and USPS.
2. For IRIS, it performs k-Nearest Neighbors (k-NN) classification using 4 features to predict the class of iris flowers.
3. For USPS, it evaluates k-NN for digit recognition on images labeled 0-9, calculating distances between test and training points for varying values of k to optimize classification.
This document demonstrates using naive Bayes classification to analyze two datasets - contacts and iris data. For each dataset, the data is split into a training set and test set. A naive Bayes classifier model is generated from the training set and used to predict the classes of the test set. The predictions are then compared to the actual classes in the test set to evaluate the accuracy of the naive Bayes model. For both datasets, the naive Bayes model is able to accurately predict most of the test instances.
The document discusses analyzing a contacts dataset using R. It loads the contacts data, explores various attributes, builds a classification tree to predict "Young" status, and discusses parameter tuning. It also loads iris data, builds a classification tree to predict species using rpart with cp=0.1, plots the tree and data, and performs prediction on a test set with over 96% accuracy.
The document provides an introduction to the R programming language. It discusses how R can be downloaded and installed on various operating systems like Mac, Windows, and Linux. It demonstrates basic functions and operations in R like arithmetic, vectors, matrices, plotting, and distributions. Examples of key functions are shown including reading data, calculating statistics, importing and exporting data, and performing linear algebra operations. Resources for learning more about R programming are also listed.
The document discusses several machine learning algorithms and techniques. It introduces classification, pattern recognition, clustering, association rule learning. It then covers decision trees in more detail, explaining the exact cover by 3-set problem, ID3 algorithm, CART, and C4.5 decision tree induction. Random forests are also mentioned briefly. Examples are provided to illustrate calculation of information gain and entropy measures.
The document contains information about k-means clustering:
(1) It describes the basic k-means clustering algorithm which assigns data points to k clusters by minimizing the within-cluster sum of squares.
(2) It provides details on how k-means clustering is implemented, including randomly initializing cluster centers, assigning points to the closest center, and recalculating centers as the mean of each cluster.
(3) It notes some of the challenges with k-means clustering, including that it does not work well for non-convex clusters and can get stuck in local optima depending on random initialization.
The document describes hierarchical clustering algorithms. It compares the single-link and complete-link algorithms. Single-link produces elongated clusters by connecting nearby points, while complete-link produces more compact clusters by only merging groups whose furthest points are close. Complete-link generally produces more useful hierarchies but is less versatile than single-link. Average linkage is also mentioned as an alternative that calculates distances between groups as the average of all point-point distances.
21. 基本データ型のラッパークラス
基本データ型 参照型
int Integer
double Double 基本データ型の
char Character ラッパークラス
boolean Boolean
スタック
なし String
Test10b.java
public class Test10b {
public static void main( String[] args ){
Integer a;
int b; ヒープ
a = 5;
b = a;
}
}
基本データ型はプリミティブ型とも呼ばれる
2008年8月,データ解析の基礎,加藤,瀬々,金子. 21
22. 基本データ型のラッパークラス
基本データ型 参照型
int Integer
double Double 基本データ型の
char Character ラッパークラス
boolean Boolean
スタック
なし String
Test10b.java a b
public class Test10b {
public static void main( String[] args ){
Integer a;
int b; ヒープ
a = 5;
b = a;
}
}
基本データ型はプリミティブ型とも呼ばれる
2008年8月,データ解析の基礎,加藤,瀬々,金子. 22
23. 基本データ型のラッパークラス
基本データ型 参照型
int Integer
double Double 基本データ型の
char Character ラッパークラス
boolean Boolean
スタック
なし String
Test10b.java a b
public class Test10b {
public static void main( String[] args ){
Integer a;
int b; ヒープ
a = 5;
b = a;
5
}
}
基本データ型はプリミティブ型とも呼ばれる
2008年8月,データ解析の基礎,加藤,瀬々,金子. 23
24. 基本データ型のラッパークラス
基本データ型 参照型
int Integer
double Double 基本データ型の
char Character ラッパークラス
boolean Boolean
スタック
なし String
Test10b.java a b=5
public class Test10b {
public static void main( String[] args ){
Integer a;
int b; ヒープ
a = 5;
b = a;
5
}
}
基本データ型はプリミティブ型とも呼ばれる
2008年8月,データ解析の基礎,加藤,瀬々,金子. 24
25. ArrayList<T> のメソッド
Test10c.java プロジェクト名: test10
new ArrayList<T>()
import java.util.*; オブジェクトを生成する
public class Test10c {
public static void main( String[] args ){
int i; .add(T x)
ArrayList<Double> v; 要素xを追加する
v = new ArrayList<Double>();
v.add( 10.0 ); .size()
v.add( 10.1 );
v.add( 10.2 );
要素数を返す
for ( i = 0; i < v.size(); i++ ){
System.out.println(quot;[quot;+i+quot;]=quot;+v.get(i)); .get( int i )
} 第i要素を返す
}
}
画面
[0]=10.0
[1]=10.1
[2]=10.2
2008年8月,データ解析の基礎,加藤,瀬々,金子. 25
26. 計算過程
Test10c.java プロジェクト名: test10
スタック
import java.util.*;
public class Test10c {
public static void main( String[] args ){ i v
int i;
ArrayList<Double> v;
v = new ArrayList<Double>();
v.add( 10.0 );
v.add( 10.1 );
v.add( 10.2 ); ヒープ
for ( i = 0; i < v.size(); i++ ){
System.out.println(quot;[quot;+i+quot;]=quot;+v.get(i));
}
}
}
画面
2008年8月,データ解析の基礎,加藤,瀬々,金子. 26
27. 計算過程
Test10c.java プロジェクト名: test10
スタック
import java.util.*;
public class Test10c {
public static void main( String[] args ){ i v
int i;
ArrayList<Double> v;
v = new ArrayList<Double>();
v.add( 10.0 );
v.add( 10.1 );
v.add( 10.2 ); ヒープ
for ( i = 0; i < v.size(); i++ ){
System.out.println(quot;[quot;+i+quot;]=quot;+v.get(i));
}
}
}
画面
2008年8月,データ解析の基礎,加藤,瀬々,金子. 27
28. 計算過程
Test10c.java プロジェクト名: test10
スタック
import java.util.*;
public class Test10c {
public static void main( String[] args ){ i v
int i;
ArrayList<Double> v;
v = new ArrayList<Double>();
v.add( 10.0 );
v.add( 10.1 );
v.add( 10.2 ); ヒープ
for ( i = 0; i < v.size(); i++ ){
System.out.println(quot;[quot;+i+quot;]=quot;+v.get(i));
}
}
}
画面 10.0
2008年8月,データ解析の基礎,加藤,瀬々,金子. 28
29. 計算過程
Test10c.java プロジェクト名: test10
スタック
import java.util.*;
public class Test10c {
public static void main( String[] args ){ i v
int i;
ArrayList<Double> v;
v = new ArrayList<Double>();
v.add( 10.0 );
v.add( 10.1 );
v.add( 10.2 ); ヒープ
for ( i = 0; i < v.size(); i++ ){
System.out.println(quot;[quot;+i+quot;]=quot;+v.get(i));
}
}
}
画面 10.0
10.1
2008年8月,データ解析の基礎,加藤,瀬々,金子. 29
30. 計算過程
Test10c.java プロジェクト名: test10
スタック
import java.util.*;
public class Test10c {
public static void main( String[] args ){ i v
int i;
ArrayList<Double> v;
v = new ArrayList<Double>();
v.add( 10.0 );
v.add( 10.1 );
v.add( 10.2 ); ヒープ
for ( i = 0; i < v.size(); i++ ){
System.out.println(quot;[quot;+i+quot;]=quot;+v.get(i));
}
}
}
画面 10.0
10.1
10.2
2008年8月,データ解析の基礎,加藤,瀬々,金子. 30
31. 準備完了
基本データ型 参照型
int Integer
double Double
char Character
boolean Boolean
なし String
ArrayList<T> のメソッド,Tは参照型のみ許される
メソッド名 説明
new ArrayList<T>() ArrayList<T>の実体を生成する
add(T x) 要素 x を追加する
size() 要素数を返す
get( int i ) 第 i 要素を返す
2008年8月,データ解析の基礎,加藤,瀬々,金子. 31
32. やりたいこと
Test09i.java
public class Test09i {
public static void main( String[] args ){
String[] seqs;
double[][] freqmat;
seqs = new String[]{quot;GTATAAAAAGCGGquot;,quot;CTATAAAAGGCCCquot;,quot;GTATAAAGGGGCGquot;,
quot;GTATATAAGCGCGquot;,quot;CTATAAAGGGGCCquot;,quot;GTATAAAGGCGGGquot;};
freqmat = comp_freqmat( seqs );
pri_freqmat( freqmat );
}
}
これまではすべてデータはプログラムに書き込んできた
データが大量になったときプログラムを書き換えるのは大変
ファイルから入力するように修正しよう
そうすれば,プログラムを大きく書き換えなくても済む!
2008年8月,データ解析の基礎,加藤,瀬々,金子. 32