OpenLLM-France/wikimedia huggingface dataset:
# Objective-C Programming

Objective-C is an object-oriented programming language. It was named after the concept of adding objects to the C language.
Objective C was introduced with NeXTSTEP and OPENSTEP, and was considerably extended in application due to its use with the Cocoa libraries under Mac OS X or the GNUstep libraries. However, you can program in Objective-C without these libraries if you wish.
Because not everyone uses OPENSTEP or Mac OS X, we will introduce these library-specific details later.
Familiarity with the C programming language is required, as Objective-C shares a lot with it, hence its name.

## Contents

* Objective-C concepts
** The basic concepts of object-oriented programming in terms of Objective-C terminology.
* Getting Started
** Invoking Objective-C compilers
* Objective-C syntax
** The basic details of how to write classes in Objective-C. Writing the interface and implementation.
* Objective-C in depth
** Objective-C techniques, using the `Object` class, categories, protocols.
* Objective-C advanced features
** Advanced Objective-C, selectors, message forwarding, archiving.

## See also

* C Programming
* C++ Programming
* Java Programming
* Programming Mac OS X_with Cocoa for Beginners
# See Also:
## Sources used in this book:

Wikipedia: Object oriented programming
https://www.thoughtco.com/what-is-inheritance-2034264
Wikipedia.org/wiki/Class_(computer_programming)
Python Programming/Classes
Wikipedia:Property(programming)
Wikipedia:Method(Computer programming)
Wikipedia:Constructors
Open Books project:Python3-Classes and Objects
Wikipedia:Mutator method
Programming Fundamentals/encapsulation
Wikipedia:Encapsulation_(computer_programming)
Wikipedia:Polymorphism_(computer_science)
Programming Fundamentals/Inheritance and Polymorphism
Wikipedia:Abstraction(computer science)
Wikipedia:Programming_paradigm
TI-Basic Z80 Programming/Advanced Programming

## Additional resources(Language Specific)
### JavaScript:

Object-oriented Programming in 7 minutes | Mosh

### Python:

Wikibooks: Introduction to Python Programming
Wikibooks: Python Programming
Unit-Testing

## Additional resources(Non-Language specific)

Wikibooks: Introduction to Programming
Wikibooks: Programming Fundamentals
# Linear Algebra/Licensing And History

For information regarding the Licensing of this book please see Wikibooks' Copyright Policy. The original text of this wikibook has been copied form the book "Linear Algebra" by:
    Jim Hefferon, Mathematics
    Saint Michael's College
    Colchester, Vermont USA 05439.
The original text is available here, and is released under either the GNU Free Documentation License or the Creative Commons Attribution-ShareAlike 2.5 License.
# Linear Algebra/Linear Dependance of Columns

Let C$_1$, C$_2$, C$_3$, ..., C$_n$ be n columns of m numbers $C_{n}={\begin{bmatrix}a_{1n}\\a_{2n}\\a_{3n}\\\vdots \\a_{mn}\\\end{bmatrix}}$.
A linear combination of columns n$_1$C$_1$+n$_2$C$_2$+n$_3$C$_3$+...+n$_n$C$_n$ is the column
$C_{n}={\begin{bmatrix}c_{1}\\c_{2}\\c_{3}\\\vdots \\c_{n}\\\end{bmatrix}}$.
Where c$_k$=n$_1$a$_{k1}$+n$_1$a$_{k1}$+n$_2$a$_{k2}$+n$_3$a$_{k3}$+...+n$_n$a$_{kn}$.

## Theorem

If there is a determinant of order n which is A=a$_{ij}$, and there are n columns of n elements such that the ith entry of the jth column is equal to a$_{ij}$, then if one of the columns is a linear combination of the other columns, then the determinant is equal to 0.

### Proof

Suppose that the kth column is a linear combination of the other column,
${\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &c_{1}a_{11}+c_{2}a_{12}+c_{3}a_{13}+\ldots +c_{n}a_{1}n&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &c_{1}a_{21}+c_{2}a_{22}+c_{3}a_{23}+\ldots +c_{n}a_{2}n&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &c_{1}a_{31}+c_{2}a_{32}+c_{3}a_{33}+\ldots +c_{n}a_{3}n&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &c_{1}a_{n1}+c_{2}a_{n2}+c_{3}a_{n3}+\ldots +c_{n}a_{n}n&\ldots &a_{nn}\\\end{bmatrix}}$.
Then by the linearity of determinants, the determinant is equal to
$c_{1}{\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &a_{11}&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &a_{21}&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &a_{31}&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &a_{n1}&\ldots &a_{nn}\\\end{bmatrix}}+c_{2}{\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &a_{12}&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &a_{22}&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &a_{32}&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &a_{n2}&\ldots &a_{nn}\\\end{bmatrix}}+c_{3}{\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &a_{13}&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &a_{23}&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &a_{33}&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &a_{n3}&\ldots &a_{nn}\\\end{bmatrix}}+\ldots +c_{n}{\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &a_{1n}&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &a_{2n}&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &a_{3n}&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &a_{nn}&\ldots &a_{nn}\\\end{bmatrix}}$.
Since all of those matrices have repeat columns, their determinants are 0, and so their sum is 0.

## Rank of a Matrix

The rank of a matrix is the maximum order of a minor that does not equal 0. The minor of a matrix with the order of the rank of the matrix is called a basis minor of the matrix, and the columns that the minor includes are called the basis columns.
# Linear Algebra/Linear Dependence

In this chapter, we will briefly treat the case of linear dependence of columns. Later, on the section on vectors, we will see what linear dependence means in general. For now, however, we consider its relevance to determinants.

## Definition

Consider m columns of numbers, each with n numbers from a field F:
$C_{1}={\begin{Vmatrix}a_{11}\\a_{21}\\...\\a_{n1}\end{Vmatrix}},C_{2}={\begin{Vmatrix}a_{12}\\a_{22}\\...\\a_{n2}\end{Vmatrix}},..,C_{m}={\begin{Vmatrix}a_{1m}\\a_{2m}\\...\\a_{nm}\end{Vmatrix}}$
We define the addition of two columns to be the column with their corresponding entries added, and we define scalar multiplication of a number in the field F with the column simply to be the column with each of their entries multiplied by that number.
We call the sum
$\alpha _{1}{\begin{Vmatrix}a_{11}\\a_{21}\\...\\a_{n1}\end{Vmatrix}}+\alpha _{2}{\begin{Vmatrix}a_{12}\\a_{22}\\...\\a_{n2}\end{Vmatrix}}+..+\alpha _{m}{\begin{Vmatrix}a_{1m}\\a_{2m}\\...\\a_{nm}\end{Vmatrix}}$
with any field elements $\alpha _{1},\alpha _{2},...,\alpha _{m}$ as a linear combination of the columns $C_{1},C_{2},...,C_{n}$
Now suppose that these columns form a determinant of order n. Then we have the following result:
Theorem: If one of the columns is a linear combination of the other, then the determinant is 0.
Proof: By a theorem proven earlier, a determinant is 0 when one of the columns (or rows) of the determinant is a linear combination of the others. Since a column which is a linear combination of the others remains a linear combination of the others in the determinant, the determinant is 0.
The converse of this theorem is also true, and we will prove this later on this page.
# Linear Algebra/Linear Dependence of Columns

Let C$_1$, C$_2$, C$_3$, ..., C$_n$ be n columns of m numbers $C_{n}={\begin{bmatrix}a_{1n}\\a_{2n}\\a_{3n}\\\vdots \\a_{mn}\\\end{bmatrix}}$.
A linear combination of columns n$_1$C$_1$+n$_2$C$_2$+n$_3$C$_3$+...+n$_n$C$_n$ is the column
$C_{n}={\begin{bmatrix}c_{1}\\c_{2}\\c_{3}\\\vdots \\c_{n}\\\end{bmatrix}}$.
Where c$_k$=n$_1$a$_{k1}$+n$_1$a$_{k1}$+n$_2$a$_{k2}$+n$_3$a$_{k3}$+...+n$_n$a$_{kn}$.

## Theorem

If there is a determinant of order n which is A=a$_{ij}$, and there are n columns of n elements such that the ith entry of the jth column is equal to a$_{ij}$, then if one of the columns is a linear combination of the other columns, then the determinant is equal to 0.

### Proof

Suppose that the kth column is a linear combination of the other column,
${\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &c_{1}a_{11}+c_{2}a_{12}+c_{3}a_{13}+\ldots +c_{n}a_{1}n&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &c_{1}a_{21}+c_{2}a_{22}+c_{3}a_{23}+\ldots +c_{n}a_{2}n&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &c_{1}a_{31}+c_{2}a_{32}+c_{3}a_{33}+\ldots +c_{n}a_{3}n&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &c_{1}a_{n1}+c_{2}a_{n2}+c_{3}a_{n3}+\ldots +c_{n}a_{n}n&\ldots &a_{nn}\\\end{bmatrix}}$.
Then by the linearity of determinants, the determinant is equal to
$c_{1}{\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &a_{11}&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &a_{21}&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &a_{31}&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &a_{n1}&\ldots &a_{nn}\\\end{bmatrix}}+c_{2}{\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &a_{12}&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &a_{22}&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &a_{32}&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &a_{n2}&\ldots &a_{nn}\\\end{bmatrix}}+c_{3}{\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &a_{13}&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &a_{23}&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &a_{33}&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &a_{n3}&\ldots &a_{nn}\\\end{bmatrix}}+\ldots +c_{n}{\begin{bmatrix}a_{11}&a_{12}&a_{13}&\ldots &a_{1n}&\ldots &a_{1n}\\a_{21}&a_{22}&a_{23}&\ldots &a_{2n}&\ldots &a_{2n}\\a_{31}&a_{23}&a_{33}&\ldots &a_{3n}&\ldots &a_{3n}\\\vdots &\vdots &\vdots &\vdots &\vdots &\vdots &\vdots \\a_{n1}&a_{n3}&a_{n3}&\ldots &a_{nn}&\ldots &a_{nn}\\\end{bmatrix}}$.
Since all of those matrices have repeat columns, their determinants are 0, and so their sum is 0.

## Rank of a Matrix

The rank of a matrix is the maximum order of a minor that does not equal 0. The minor of a matrix with the order of the rank of the matrix is called a basis minor of the matrix, and the columns that the minor includes are called the basis columns.
# Linear Algebra/Jordan Form

This section uses material from three optional subsections: Direct Sum, Determinants Exist, and Other Formulas for the Determinant.
The chapter on linear maps shows that every $h:V\to W$ can be represented by a partial-identity matrix with respect to some bases $B\subset V$ and $D\subset W$. This chapter revisits this issue in the special case that the map is a linear transformation $t:V\to V$. Of course, the general result still applies but with the codomain and domain equal we naturally ask about having the two bases also be equal. That is, we want a canonical form to represent transformations as ${\rm {Rep}}_{B,B}(t)$.
After a brief review section, we began by noting that a block partial identity form matrix is not always obtainable in this $B,B$ case. We therefore considered the natural generalization, diagonal matrices, and showed that if its eigenvalues are distinct then a map or matrix can be diagonalized. But we also gave an example of a matrix that cannot be diagonalized and in the section prior to this one we developed that example. We showed that a linear map is nilpotent— if we take higher and higher powers of the map or matrix then we eventually get the zero map or matrix— if and only if there is a basis on which it acts via disjoint strings. That led to a canonical form for nilpotent matrices.
Now, this section concludes the chapter. We will show that the two cases we've studied are exhaustive in that for any linear transformation there is a basis such that the matrix representation ${\rm {Rep}}_{B,B}(t)$ is the sum of a diagonal matrix and a nilpotent matrix in its canonical form.
# Pinyin/Wall Street

Wall Street is the US financial and stock center in New York.
Wall Street shih zaih New York d US d jinrorng her guupiaoh zhongxin.
# Pinyin/Walnut

Search Pinyin walnut
# Pinyin/Warning

Search Pinyin warning
* jiinggaoh
* jiingbaoh
# Pinyin/Want

to want to: to have a desire or a wish for something
xiaangyaoh: xiwahng derdaoh mooushihwuh
# Pinyin/War

Search Pinyin war
# Pinyin/Warhead

See also
# Pinyin/Walking

See also
# Microsoft Office/Create a Pivot Table

A pivot table is a great reporting tool that sorts and sums independent of the original data layout in the spreadsheet. It is an interactive representation of a data table. One can rearrange the data and choose what to display and what to hide. The best way to find out what a pivot table can do is to create one.
First, set up categories and create some data (See Table 1):
Table 1: Original Data :
| Who | Week | What | Amount Spent |
| Joe | 3 | Activities | $18 |
| Beth | 4 | Food | $17 |
| Janet | 5 | Activities | $14 |
| Joe | 3 | Food | $12 |
| Joe | 4 | Activities | $19 |
| Janet | 5 | Car | $12 |
Now choose any cell in this table and choose Data > Pivot Table wizard. Pivot Table should be selected. Click Next. Excel asks for the data source and suggests this table. Click Next. Finally Excel asks if the table should be placed in a new worksheet. Select new sheet. Click Finish.

## Arranging data on the table

Drag the headers Who, Week and What into the ROW area, and the Amount header into the Data area. (Leave the Column area blank for now.) To change the placement of an item, drag the header title to the desired area. If the Amount tag does not show "Sum of Amount", double-click it and choose the Sum option.
Double-clicking the headers gives options of showing/hiding specific data (like Empty and Activities, may come in handy) and removing subtotaling for this column. Right-clicking gives other options, among them Hide and Show Detail for reading totals only.
Here comes another useful pivot made from the same list. Select any item in the original data list and choose Pivot Table wizard again. This time, drag Who into the Row field, What into the Column field and Amount into the Data field. This table is also very useful for graphing.
# Lesson 07.6: The verb υπάρχω, to exist

The verb υπάρχω, to exist, is used where we would say there is or there are in English.
| Υπάρχουν αγελάδες στην ύπαιθρο. | There are cows in the countryside. |
| Υπάρχει κανένα πουλί στο δέντρο; | Is there any bird οn the tree? (lit. Is there no bird on the tree?) |
| Σκέπτομαι, άρα υπάρχω. | I think, therefore I am. |
# Lesson 8.4: Eating and Drinking in Greece
## The verb τρώ(γ)ω, to eat

Present tense:
| τρώω | τρώμε |
| τρως | τρώτε |
| τρώει | τρώνε |
Imperfect:
| έτρωγα | τρώγαμε |
| έτρωγες | τρώγατε |
| έτρωγε | έτρωγαν |
Past (The aorist stem is φαγ-):
| έφαγα | φάγαμε |
| έφαγες | φάγατε |
| έφαγε | έφαγαν |
Notice the έ- prefix on the past tense of all persons except first and second person plural. Example:
| Ο Αδάμ και η Εύα έφαγαν το φρούτο. | Adam and Eve ate the fruit. |

## The verb πίνω, to drink

Present tense:
| πίνω | πίνουμε |
| πίνεις | πίνετε |
| πίνει | πίνουν |
Imperfect:
| έπινα | πίναμε |
| έπινες | πίνατε |
| έπινε | έπιναν |
Past:
| ήπια | ήπιαμε |
| ήπιες | ήπιατε |
| ήπιε | ήπιαν |
Example:
| Ο Σωκράτης ήπιε το κώνειο. | Socrates drank the hemlock. |
(The scientific name for hemlock is conium maculatum, which is Latin for "poison hemlock." A very popular rock band in Greece during the sixties and seventies bore the name "Socrates drank the conium.")
Contents Lesson 1 • Lesson 2 • Lesson 3 • Lesson 4 • Lesson 5 • Lesson 6 • Lesson 7 • Lesson 8 • Lesson 9 • Lesson 10 Vocab 1 • Vocab 2 • Vocab 3 • Vocab 4 • Vocab 5 • Vocab 6 • Vocab 7 • Vocab 8 • Vocab 9 Alphabet • Grammar Credits
# Modern Greek/Lesson 09.2
## Aorist

The aorist is the simple past in English. It is used for:
* Narrating past events (those that lie entirely in the past)
Note that the aorist stem is not just associated with the past, it is also used:
* In subordinating constructions such as "θα" and "θέλω/μπορώ να".

## 1st Conjugation

The past tense is used to indicate an action that occurred at one time in the past, or that has been completed. It is formed in the same way as the imperfect, but from the aorist stem.
Past:
| διάβασα | διαβάσαμε |
| διάβασες | διαβάσατε |
| διάβασε | διάβασαν |
Example:
| Χτες, διάβασε την εφημερίδα. | Yesterday, he read the newspaper. |

## Formation of the aorist stem in the first conjugation

We have already seen that aorist stems are often formed by adding an "s" sound. The following list shows more of the common patterns:
| λύνω | λύσω | έλυσα | loosen |
| θέτω | θέσω | έθεσα | put, place |
| ακούω | ακούσω | άκουσα | hear |
| νιώθω | νιώσω | ένιωσα | feel |
| διαβάζω | διαβάσω | διάβασα | read |
| αλλάζω | αλλάξω | άλλαξα | change |
| υπάρχω | υπάρξω | υπήρξα | exist |
| ανοίγω | ανοίξω | άνοιξα | open |
| επιδιώκω | επιδιώξω | επίδιωξα | aim |
| σπρώχνω | σπρώξω | έσπρωξα | push |
| διδάσκω | διδάξω | δίδαξα | teach |
| γράφω | γράψω | έγραψα | write |
| λείπω | λείψω | έπειψα | lack |
| σκύβω | σκύψω | έσκυψα | lean, bend over |
Examples:
| Έλυσαν τις ζώνες τους. | They loosened their belts. |
| Μπορούμε να ακούσουμε τους σκύλους. | We can hear the dogs. |
| Ο Ευκλείδης ήταν Έλληνας μαθηματικός, που δίδαξε στην Αλεξάνδρεια της Αιγύπτου. Στις μέρες μας είναι γνωστός ως ο πατέρας της γεωμετρίας. | Euclid was a greek mathematician who taught in Alexandria, Egypt. Today (lit. "in our days") he is known as the father of geometry. |

## 2nd Conjugation

Verbs use one of the suffixes -ασ-, -ησ, -εσ-. The accent also goes to the previous syllable:
The verb αγαπώ (to love) becomes αγάπησα
| αγάπησα | αγαπήσαμε |
| αγάπησες | αγαπήσατε |
| αγάπησε | αγάπησαν |
The verb διψώ (to go thirsty) becomes δίψασα
| δίψασα | διψάσαμε |
| δίψασες | διψάσατε |
| δίψασε | δίψασαν |
The verb καλώ (to call, to invite) becomes κάλεσα
| κάλεσα | καλέσαμε |
| κάλεσες | καλέσατε |
| κάλεσε | κάλεσαν |
Examples:
| Η μητέρα κάλεσε το παιδί της. | The mother called her child. |
| Τα παιδιά δίψασαν. | The children went thirsty. |
Contents Lesson 1 • Lesson 2 • Lesson 3 • Lesson 4 • Lesson 5 • Lesson 6 • Lesson 7 • Lesson 8 • Lesson 9 • Lesson 10 Vocab 1 • Vocab 2 • Vocab 3 • Vocab 4 • Vocab 5 • Vocab 6 • Vocab 7 • Vocab 8 • Vocab 9 Alphabet • Grammar Credits
# Perfect and Pluperfect

As in English, the perfect tenses are formed using the helping verb to have.
The Perfect tense (same as Present Perfect in English) is called Παρακείμενος (parakeimenos, "being close") is formed by the verb έχω in present tense followed by the third person singular of the past subjunctive of the verb. The past subjunctive is formed by the aorist stem plus the suffix -ει.
| Έχω γραψει. | I have written. |
| Έχουμε γραψει. | We have written. |
The Pluperfect tense (same as Past Perfect in English) is called Υπερσυντέλικος (hypersyndelikos, "hyper-perfect") is formed by the verb έχω in past tense followed by the third person singular of the past subjunctive of the verb
| Είχα γραψει. | I had written. |
| Είχαμε γραψει. | We had written. |
| Είχε αποχαιρετήσει τη γυναικα του. | He had said goodbye to his wife. |
Contents Lesson 1 • Lesson 2 • Lesson 3 • Lesson 4 • Lesson 5 • Lesson 6 • Lesson 7 • Lesson 8 • Lesson 9 • Lesson 10 Vocab 1 • Vocab 2 • Vocab 3 • Vocab 4 • Vocab 5 • Vocab 6 • Vocab 7 • Vocab 8 • Vocab 9 Alphabet • Grammar Credits
# Family
## Vocabulary
## Examples

Note that in Greek, a definite article is used with a person's name.
Contents Lesson 1 • Lesson 2 • Lesson 3 • Lesson 4 • Lesson 5 • Lesson 6 • Lesson 7 • Lesson 8 • Lesson 9 • Lesson 10 Vocab 1 • Vocab 2 • Vocab 3 • Vocab 4 • Vocab 5 • Vocab 6 • Vocab 7 • Vocab 8 • Vocab 9 Alphabet • Grammar Credits
# Active Imperfect Tense
## 1st Conjugation

The imperfect tense is used to describe an action in the past that was continuous or repeated. For verbs in the first conjugation, it is formed from the progressive stem by moving the accent to the third syllable from the end, and adding endings that differ from those used in the present. The following example illustrates this with the verb διαβάζω (to read):
Present:
| διαβάζω | διαβάζουμε |
| διαβάζεις | διαβάζετε |
| διαβάζει | διαβάζουν |
Imperfect:
| διάβαζα | διαβάζαμε |
| διάβαζες | διαβάζατε |
| διάβαζε | διάβαζαν |
Example:
| Η γιαγιά μου διάβαζε. | My grandmother used to read. |
If the stem of the verb is too short to allow an accent on the third syllable from the end, the prefix ε- is added, take for example the verb γράφω (to write):
| έγραφα | γράφαμε |
| έγραφες | γράφατε |
| έγραφε | έγραφαν |

## 2nd Conjugation

The Imperfect is the Past Continuous in English.
Both first order verbs like αγαπώ (to love) and second order verbs like καλώ (to call), use the suffix -ούσ-:
| καλούσα | καλούσαμε |
| καλούσες | καλούσατε |
| καλούσε | καλούσαν |
Examples:
| Η μητέρα καλούσε το παιδί της. | The mother was calling her child. |
| Το παιδί αγαπούσε τη μητέρα του. | The child loved his mother. |

# Passive Imperfect Tense

Review of the present passive; εμπιστεύομαι, to trust:
| εμπιστεύομαι | εμπιστευόμαστε |
| εμπιστεύεσαι | εμπιστεύεστε |
| εμπιστεύεται | εμπιστεύονται |
The passive imperfect, like the active imperfect, indicates an action in the past that was ongoing:
| εμπιστευόμουν | εμπιστευόμασταν |
| εμπιστευόσουν | εμπιστευόσασταν |
| εμπιστευόταν | εμπιστεύονταν |
Contents Lesson 1 • Lesson 2 • Lesson 3 • Lesson 4 • Lesson 5 • Lesson 6 • Lesson 7 • Lesson 8 • Lesson 9 • Lesson 10 Vocab 1 • Vocab 2 • Vocab 3 • Vocab 4 • Vocab 5 • Vocab 6 • Vocab 7 • Vocab 8 • Vocab 9 Alphabet • Grammar Credits
# Lesson 8.5: Days/Months Part II

In this lesson we will learn some more vocabulary and expressions relating to the dates.

## Describing when things happened

Since June - από τον Ιούνιο For the last 2 years - εδώ και δύο χρόνια 3 weeks ago - πρίν 3 εβδομάδες

## Summary

* We have TBC

## Exercises

Now try the exercise found here.
Contents Lesson 1 • Lesson 2 • Lesson 3 • Lesson 4 • Lesson 5 • Lesson 6 • Lesson 7 • Lesson 8 • Lesson 9 • Lesson 10 Vocab 1 • Vocab 2 • Vocab 3 • Vocab 4 • Vocab 5 • Vocab 6 • Vocab 7 • Vocab 8 • Vocab 9 Alphabet • Grammar Credits
# Lesson 9.5: Past tenses of είμαι and έχω

The verbs είμαι and έχω have only a single past tense, rather than separate imperfect and past tenses.

## Summary

* We have learnt

## Exercises

Now try the exercises here.

## Notes

* ^ from http://el.wikipedia.org/wiki/Αχιλλέας
Contents Lesson 1 • Lesson 2 • Lesson 3 • Lesson 4 • Lesson 5 • Lesson 6 • Lesson 7 • Lesson 8 • Lesson 9 • Lesson 10 Vocab 1 • Vocab 2 • Vocab 3 • Vocab 4 • Vocab 5 • Vocab 6 • Vocab 7 • Vocab 8 • Vocab 9 Alphabet • Grammar Credits
# Lesson 10.1: The Simple Future

In this lesson we'll introduce how to talk about things that will happen. I.e. the future tense.

## Type I

The simple future is formed from Θα + aorist (simple) stem + the present endings.

## Summary

* We have learnt TBC

## Exercises

Now try the exercises here.
Contents Lesson 1 • Lesson 2 • Lesson 3 • Lesson 4 • Lesson 5 • Lesson 6 • Lesson 7 • Lesson 8 • Lesson 9 • Lesson 10 Vocab 1 • Vocab 2 • Vocab 3 • Vocab 4 • Vocab 5 • Vocab 6 • Vocab 7 • Vocab 8 • Vocab 9 Alphabet • Grammar Credits
# Lesson 8.2: Buying Something

In this lesson you'll learn how to buy something and how to ask how much something costs.

## I buy - αγοράζω

The verb "to buy" is αγοράζω and is a regular class I verb in the present tense (see the lesson here. It's summarized below.

## How much does it cost?

To ask how much something costs you can use κάνει/κάνουν (singlur/plural) or κοστίζει/κοστίζουν (again singular/plural). Both of these verbs are regular type I in the present tense.

## Useful adjectives

The following table contains some useful vocabulary.

## Summary

* We have learnt the verb αγοράζω (to buy).
* We have learnt how to ask how much something costs.
* We've practiced using the numbers that we learnt in 6.1 to 6.3.

## Exercises

Now try the exercise here.
Contents Lesson 1 • Lesson 2 • Lesson 3 • Lesson 4 • Lesson 5 • Lesson 6 • Lesson 7 • Lesson 8 • Lesson 9 • Lesson 10 Vocab 1 • Vocab 2 • Vocab 3 • Vocab 4 • Vocab 5 • Vocab 6 • Vocab 7 • Vocab 8 • Vocab 9 Alphabet • Grammar Credits