OpenLLM-France/wikimedia huggingface dataset:
# Data Mining Algorithms In R/Packages/RWeka/Weka stemmers
## Description

R interfaces to Weka stemmers.

## Usage

IteratedLovinsStemmer(x, control = NULL) LovinsStemmer(x, control = NULL)

## Arguments

x, a character vector with words to be stemmed.
control, an object of class Weka_control, or a character vector of control options, or NULL (default).

## Details

The R interface to Weka’s Snowball stemmers is available as function SnowballStemmer in package Snowball.

## Value

A character vector with the stemmed words.
# Data Mining Algorithms In R/Packages/RWeka/Weka tokenizers
## Description

R interfaces to Weka tokenizers.

## Usage

AlphabeticTokenizer(x, control = NULL)
NGramTokenizer(x, control = NULL)
WordTokenizer(x, control = NULL)

## Arguments

x, a character vector with strings to be tokenized.
control, an object of class Weka_control, or a character vector of control options, or NULL (default).

## Details

AlphabeticTokenizer is an alphabetic string tokenizer, where tokens are to be formed only from contiguous alphabetic sequences.
NGramTokenizer splits strings into n-grams with given minimal and maximal numbers of grams.
WordTokenizers is a simple word tokenizer.

## Value

A character vector with the tokenized strings.
# Data Mining Algorithms In R/Packages/arules
## Contents

* combine
* affinity
# Data Mining Algorithms In R/Packages/arules/adult
## Case Study
### Scenario
### Analysis

We have observed that CSPADE found many trivial sequences from user behaviour. For example, it has found many unitary sequences, such as <{design}>, <{ajax}>, <{css}>, among others. These unitary sequences are really frequently used, but they may not be useful in the particular application, which is Tag Recommendation.
Furthermore, other trivial sequences were found, such as <{design}.{design}> and <{webdesign},{design}>. These sequences indicates that the same users tend to bookmarks pages in the same subject subsequently. However, some interesting patters were also found. We can cite <{library},{books}>, <{javascript},{ajax}> and <{video},{youtube}>.
We can also observe that many frequent patterns are related to design, art and web_development. These tags are also the most popular tags in the whole Delicious system, as can be seen here.

## References

* ^ Li, K.-C., Chang, D.-J., Rouchka, E. C., Chen, Y. Y., 2007. "Biological sequence mining using plausible neural network and its application to exon/intron boundaries prediction". In: CIBCB. IEEE, pp. 165–169.
* ^ Peng, W.-C., Liao, Z.-X., 2009. "Mining sequential patterns across multiple sequence databases". Data Knowl. Eng. 68 (10), 1014–1033.
* ^ Telecom Paper, January 2009. "Google query volume".
* ^ Zaki, M. J., 2001. "Spade: An efficient algorithm for mining frequent sequences". In: Machine Learning. Vol. 42. pp. 31–60.
# Data Mining Algorithms In R/Packages/gausspred
## Description

This software is used to predict the discrete response based on selected high dimensional features, such as gene expression data. The data are modeled with Bayesian Gaussian models. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features will appear stronger. This package provides a way to avoid this bias and yields well-calibrated prediction for the test cases when one uses F-statistic to select features.

## Requirements

R (>= 2.8.1)

## General API

* assess prediction
* data gau
* train pred gau
# Data Mining Algorithms In R/Packages/RWeka/Write.arff
## Description

Writes data into Weka Attribute-Relation File Format (ARFF) files.

## Usage

write.arff(x, file, eol = "\n")

## Arguments

x, the data to be written, preferably a matrix or data frame. If not, coercion to a data frame is attempted.
file, either a character string naming a file, or a connection. "" indicates output to the standard output connection.
eol, the character(s) to print at the end of each line (row).
# Data Mining Algorithms In R/Packages/RWeka/dot
## Description

Write a DOT language representation of an object for processing via Graphviz.

## Usage

write_to_dot(x, con = stdout, ...)

## Arguments

x, an R object.
con, a connection for writing the representation to.
Additional arguments to be passed from or to methods.

## Details

Graphviz (http://www.graphviz.org) is open source graph visualization software providing several main graph layout programs, of which dot makes “hierarchical” or layered drawings of directed graphs, and hence is typically most suitable for visualizing classification trees.
# Data Mining Algorithms In R/Packages/TreeRank
## Description

Implementation of the TreeRank methodology for building tree-based ranking rules for bipartite ranking through ROC curve optimization.

## Depends

rpart, tcltk, igraph, colorspace, tkrplot, kernlab, coin.

## General API

* LeafRank
* TRdata
* TreeRank
* TRGui
* TwoSample
# ERP Internals/Data Tables/Currency/Currency

This table contains one record for every currency the ERP package supports.
Used by: Currency, General Ledger
Key: Currency Code
Fields:
* Currency Code (a unique identifier assigned by the user)
* Symbol
* Name
* Number of Decimal Places
* Symbol Position
* Thousands Separator
* Decimal Separator
* Negative Display
* Master Currency (Yes/No)
# ERP Internals/Data Tables/Customer/Customer Interaction

This table contains one record for every interaction between the business and the customer. This table is key to Customer Relationship Marketing.
Used by: Accounts Receivable, CRM
Key: Customer Number, Date/Time
Fields:
* Customer Number
* Date/Time
* Type (Telephone,Face-to-face,etc.)
* Employee Number(s)
* Location
* Customer Attendees
* Notes
# ERP Internals/Data Tables/Customer/Customer Location

This table contains one record for every customer location.
Used by: Accounts Receivable, Order Entry/Invoicing
Key: Customer Number, Customer Location Number
Fields:
* Customer Number (a unique identifier assigned by the user)
* Customer Location Number (a unique identifier assigned by the user)
* Type
# ERP Internals/Data Tables/Currency

This consists of a group of tables as follows:
* Currency
* Currency Rate
# ERP Internals/Data Tables/Currency/Currency Rate

This table contains one record for every rate of every currency.
Used by: Currency, General Ledger
Key: Currency Code, Date/Time
Fields:
* Currency Code
* Date/Time
* Conversion Rate
# ERP Internals/Data Tables/Customer

This includes a group of tables. They are listed below:
* Customer
* Customer Interaction
* Customer Location
* Customer Person
In some enterprises customers are individual people. In others, they are other enterprises with one or more locations and contacts. Finally, many enterprises have a mixture of both individuals and enterprises. This group of tables is structured so that each customer can have one or more locations that have one or more contacts to accommodate all three possibilities.
Donors In the case of not-for-profit enterprises "donors" are "customers" (i.e. the source of revenue). They use fund-raising, grant management, and donor management to raise funds.
# ERP Internals/Data Tables/Customer/Customer

This table contains one record for every customer.
Used by: Accounts Receivable, Order Entry/Invoicing
Key: Customer Number
Fields:
* Customer Number (a unique identifier assigned by the user)
* Type
* Organization/Individual (individual must have exactly one person record)
* Active (Yes/No)
* Territory
* Group (used to group national accounts together)
* Name
* Address 1
* Address 2
* City
* State/Province
* Country
* Zip/Postal Code
* Shipping Address (same fields as above billing address)
* Telephone
* 1-800 Number
* FAX
* E-mail
* Website
* Price List
* Discount Percentage
* Produce Statement (Yes/No)
* Sales Representative Number
* Credit Limit
* Usual Terms
* Usual FOB
* Usual Ship Via
* Usual Currency
* Sales Representative ID
* Backorders Allowed (Yes/No)
* COD (Yes/No)
* NAICS
* Referred By
* Preferred Invoice Delivery Mode
* Language
* Current Accounts Receivable Balance (a calculated field that the user cannot change)
* YTD Sales (a calculated field that the user cannot change)
* Customer Since (date)
* Holdback Percentage
* Holdback Days
* GL Distribution Group (contains all the postable account numbers)
* Notes
# ERP Internals/Data Tables/Customer/Customer Person

This table contains one record for every person at every customer.
Used by: Accounts Receivable, Order Entry/Invoicing
Key: Customer Number, Customer Location Number, Person Number
Fields:
* Customer Number
* Customer Location Number
* Person Number (a unique identifier assigned by the user)
* Last Name
* First Name
* Informal Name
* Job Title
* Department
* Title (Mr/Ms/Mrs/etc.)
* Address 1
* Address 2
* City
* State/Province
* Country
* Zip/Postal Code
* Business Telephone
* Telephone Extension
* Home Telephone
* FAX
* Mobile
* Pager
* E-mail
* Marital Status
* Birthday
* Language
* Notes
# ERP Internals/Data Tables/Contact/Contact Interaction

This table contains one record for every interaction between the business and the contact. Although contacts are not yet customers, this table is key to Customer Relationship Marketing.
Used by: Contacts
Key: Contact Number, Date/Time
Fields:
* Contact Number
* Date/Time
* Type (Telephone,Face-to-face,etc.)
* Employee Number(s)
* Location
* Contact Attendees
* Notes
# Econometric Theory/Solutions to Heteroskedasticity

There are different solutions for heteroskedasticity depending on whether the form of the perturbations are known or now and whether there is a large sample size.
# Econometric Theory/Statistical Concepts

This section introduces some statistical concepts that will be useful later in this course. The contents are shown without proof and that can be found in many excellent books on that subject.
# Econometric Theory/Statistical Inference

Statistical inference is the attempt of making a statement about a population using only sample data that is a subset of that population. This is necessary since in most situations it is either impractical or expensive (or both) to collect data on the whole population.
Statistical inference is broken up into the following parts:
* Identification of the problem.
* Identification of the population.
* Specification of the statistical properties of the parameters in question.
# Omission of relevant variables

* The omission of relevant variables can cause ßs to be biased, E[ßhat] is not equal to ß; this is a more serious cost than the loss of degrees of freedom from including irrelevant variables.

# Inclusion of irrelevant variables

* The cost of including irrelevant variables is a loss of degrees of freedom.
* With every additional explanatory variable, R^2 increases (one should be aware of this so as to not take the increase of R^2 to mean something very good).
# Econometric Theory/Statistical Inference/Hypothesis Testing
## Basic Concepts

To conduct a successful hypothesis test, the following are required:
* Testable Hypothesis
We need to have a null ($H_{0}$) and alternate ($H_{1}$) hypothesis.
* Feasible test statistic
A test statistic is a random variable whose value for given sample data determines whether the null is rejected or retained. It is feasible when:
** Its probability distribution is known when the null hypothesis ($H_{0}$) is true.
** Its value can be calculated from the given sample data
* Decision rule
A decision rule clearly delineates the:
** Rejection region - the set of values of the test statistic for which $H_{0}$ is to be rejected.
** Non-rejection region - the set of values of the test statistic for which the $H_{0}$ is to be retained.

## Procedure for testing a hypothesis

* Formulate $H_{0}$ and $H_{1}$.
* Specify the test statistic and its distribution.
* Calculate the sample value of the test statistic under $H_{0}$ for the given sample data.
* Select a significance level (α) and determine the corresponding critical values (for the particular distribution).
* Apply the decision rule and state the conclusion (or inference) implied by the sample value of the test statistic.

## Notes

* The null hypothesis will always be the position where there is an equality (either strong or weak), and the alternate hypothesis will have the inequality.
# Econometric Theory/Statistical Packages

Many statistical programs are available for data analysis and statistical computing, including a variety of commercially distributed software: Eviews, SAS, Stata, RATS, TSP, WinBUGS and SPSS, and open source packages, such as Gretl and R.

## Wikibooks

* Statistical Analysis: an Introduction using R
* R Programming
* Stata
* SAS

## Wikipedia Links

* Econometrics
* Statistics Software

## Software

* SHAZAM
* Eviews
* SAS
* Stata
* RATS
* TSP
* WinBugs
* SPSS
* gretl: an open-source statistical package, mainly for econometrics

## External links

* SHAZAM Software
* R Project
* SAS Software
* SPSS Software
* Stata Software
* Econometric Links
* Free Statistics
* Gnu Regression, Econometrics and Time-series Library
| ← Statistical Packages | Econometric Theory | Prerequisite → |
# Econometric Theory/SLR

${\begin{aligned}&Y_{i}=\beta _{1}+\beta _{2}X_{i}+U_{i}\\&E(U|X)=0\\&Var(U|X)=Var(Y|X)=\sigma ^{2}\\&Y_{i}=\beta _{1}+\beta _{2}X_{i}+U_{i}\\&{\widehat {Y_{i}}}={\widehat {\beta _{1}}}+{\widehat {\beta _{2}}}X_{i}\\&{\widehat {U_{i}}}=Y_{i}-{\widehat {Y_{i}}}\\\end{aligned}}$
Use of $\beta _{1}$ or $\beta _{0}$ to denote the Y-intercept is solely discretionary.
${\begin{aligned}&{\widehat {\beta _{2}}}={\frac {\sum {(Y_{i}-{\bar {Y}}})(X_{i}-{\bar {X}})}{\sum {(X_{i}-{\bar {X}})^{2}}}}\\&{\widehat {\beta _{1}}}={\bar {Y}}-{\widehat {\beta _{2}}}{\bar {X}}\\&Var({\widehat {\beta _{2}}})={\frac {\sigma ^{2}}{\sum {(X_{i}-{\bar {X}})^{2}}}}\\&Var({\widehat {\beta _{1}}})={\frac {\sum {X_{i}^{2}}*\sigma ^{2}}{n*\sum {(X_{i}-{\bar {X}})^{2}}}}\\&{\widehat {\sigma ^{2}}}={\frac {\sum {\widehat {U_{i}^{2}}}}{n-2}}\\\end{aligned}}$
U and $\epsilon$ have both been used to denote the error term.
${\begin{aligned}&S_{2}^{2}={\widehat {Var({\widehat {\beta _{2}}})}}={\frac {\widehat {\sigma ^{2}}}{\sum {(X_{i}-{\bar {X}})^{2}}}}\\&S_{1}^{2}={\widehat {Var({\widehat {\beta _{1}}})}}={\frac {\sum {X_{i}^{2}}*{\widehat {\sigma ^{2}}}}{n*\sum {(X_{i}-{\bar {X}})^{2}}}}\\&S.E.({\widehat {\beta _{2}}})={\sqrt {\widehat {Var({\widehat {\beta _{2}}})}}}\\&S.E.({\widehat {\beta _{1}}})={\sqrt {\widehat {Var({\widehat {\beta _{1}}})}}}\\\end{aligned}}$
$S^{2}$ is used to denote a sample variance, S.E. standard error.
${\begin{aligned}&TSS=\sum {(Y_{i}-{\bar {Y}})^{2}}\\&ESS=\sum {({\widehat {Y_{i}}}-{\bar {Y}})^{2}}\\&RSS=\sum {\widehat {U_{i}^{2}}}\\\end{aligned}}$
TSS may also be presented as SST for the Total Sum of Squares, ESS as SSE (error) and RSS as SSR (residuals). Depending on the text, ESS and RSS may become very confusing, as there is great variety in the terminology used.
$R^{2}={\frac {ESS}{TSS}}=1-{\frac {RSS}{TSS}}$
${\widehat {\log(Y)}}={\widehat {\beta _{1}}}+{\widehat {\beta _{2}}}\log(X)$
# Econometric Theory/Sample Points, Sample Space and Events

The population or the sample space refers to the set of all possible outcomes of an experiment that is random. If you toss a coin there are two possible outcomes: head (H) or tail (T). If you toss two coins, there are four outcomes: HH, HT, TH and TT, where the first letter refers to the outcome of the first tossing and the second to that of the second tossing. Each outcome is called a sample point.
An event is a subset of a sample space. If we want to list all sample points with the occurrence of at least one H, we have HH, HT and TH. Any events are called mutually exclusive if, for any one trial of the experiment, the occurrence of one event implies the non-occurrence of the other event. For instance, if we got a head on one occasion, we could not simultaneously get a tail. Note: To say that any 2 events are independent means something else entirely.
Events are exhaustive if there is no other possible event. For instance, the events two heads (HH), one head and one tail (HT and TH), and two tails (TT) are all possible outcomes if tossing the coin twice. Thus, the list of events is exhaustive.

## Bibliography

* Gujarati, D.N. (2003). Basic Econometrics, International Edition - 4th ed. McGraw-Hill Higher Education. p. 870. ISBN 0-07-112342-3.
# Econometric Theory/Solutions to Multicollinearity

* Making composite variable, variables formed of those correlated explanatory variables
* Remove an explanatory variable that is correlated with another one
* Drop redundant variable and this applies to the case that two or more variables in an equation are measuring essentially the same thing
# Data Mining Algorithms In R/Dimensionality Reduction

* Principal Component Analysis
* Singular Value Decomposition
* Feature Selection
# Data Mining Algorithms In R/Frequent Pattern Mining
## Contents

* The Eclat Algorithm
* arulesNBMiner
* The Apriori Algorithm
* The FP-Growth Algorithm
# Evans Gambit: Hein Countergambit
### 4...d5

This very fun counterattacking option against the Evans Gambit is called the Hein Countergambit. Here black attacks both the Bc4 and the pawn e4. White has a couple of moves:
* 5. exd5
* 5. bxc5
* 5. Bxd5
* 5. c3!?
* 5. Bb3!?
5.exd5 removes the attacker of the c4 bishop, and counterattacks the knight. Black has two pieces hanging in the middle of the board - however, black can escape with 5...Nxb4, saving the knight and remove the attacker of the C5 bishop. 5. bxc5 opts for a bishop trade, but after the trade, the c4 and C5 pawns are both double and weak, and can be attacked easily. 5. Bxd5 captures a pawn - however black can also capture a pawn like with 5...Bxb4 and material is even. 5. c3!? is a dubious choice against the Hein Countergambit as it doesn't deal with most threats, however it defends the pawn B4 that could have been captured by the Bc5. 5. Bb3!? is another dubious choice against the Hein Countergambit that retreats the bishop to a safe square but it leaves a pawn hanging, but material is eventually equal (5...Bxb4 6. exd5 (6...Qxd5?? is not possible due to 7. Bxd5) or 5...dxe4 6. bxc5 exf3 7. Qxf3).
1.e4 e5 2.Nf3 Nc6 3.Bc4 Bc5 4.b4 d5
# Giuoco Piano
## 4... Nf6

The most common reply. It attacks the pawn on e4, develops the knight, and allows black to castle later on.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation.
1. e4 e5 2. Nf3 Nc6 3. Bc4 Bc5 4. c3 Nf6
| | 5 | 6 | 7 | 8 |
| Center Attack | d4 exd4 | cxd4 Bb4+ | Bd2 Nxe4 | Bxb4 Nxb4 | = |
| Giuoco Pianissimo | d3 d6 | O-O O-O | h3 h6 | Re1 Be6 | = |
| Bird's Attack | b4 Bb6 | d3 d6 | a4 a5 | b5 Ne7 | = |
| Albin Gambit | O-O Nxe4 | d4 Nf6 | d4 Nf6 | d4 Nf6 | = |
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# Dubov Gambit
## 6...Be7

Black retreats the bishop to e7. This retreat is generally considered safer, as it blocks any checks on the e file and controls the g5 square. Black will give back the d pawn and try to achieve d5.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation.
1. e4 e5 2. Nf3 Nc6 3. Bc4 Bc5 4. c3 Nf6 5. d4 exd4 6. b4 Be7
| 7 | 8 | 9 | 10 |
| e5 Ne4 | b5 Na5 | Bd3 Nxc3 | Nxc3 dxc3 | = |
| e5 Ne4 | b5 Na5 | Bd3 Nc5 | cxd4 Nxd3 | = |
| e5 d5 | exf6 Bxf6 | b5 Ne7 | Bd3 dxc3 | = |
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# Giuoco Piano
## 4. c3

White reinforces the d4 square, and prepares d4 in order to establish two pawns in the centre. It also stops any Nd4 by black, in the future.
White also prepares b4 to try and trap the Black bishop. White's secondary aim is to bring the queen to b3, thus intensifying the pressure on Black's f7-pawn.
A drawback to this move is that the pawn occupies a square that should be reserved for the queen's knight.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation.
1. e4 e5 2. Nf3 Nc6 3. Bc4 Bc5 4. c3
| | 4 | 5 | 6 | 7 |
| | ... Nf6 | d3 d6 | O-O O-O | Re1 Bg4 | = |
| Closed | ... Qe7 | d4 Bb6 | O-O d6 | Bg5 Nf6 | += |
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## References

* Kasparov, Garry, & Keene, Raymond 1989 Batsford chess openings 2. ISBN 0-8050-3409-9.
# Center Attack
## 5. d4

White attacks the center with 5. d4, attempting to create a strong pawn center with the d4 and e4 pawns. Black has to respond with exd4, or else surrender the entire center to White.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation.
1. e4 e5 2. Nf3 Nc6 3. Bc4 Bc5 4. c3 Nf6 5. d4
| 5 | 6 | 7 | 8 |
| ... exd4 | cxd4 Bb4+ | Bd2 Nxe4 | Bxb4 Nxb4 | = |
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# Dubov Gambit
## 6. b4

White sacrifices the d4 pawn for quick development and an open d file. White also attacks the Black bishop on c5, forcing it to move. Black can play either 6...Bb6 or 6...Be7.
In contrast to the Evans gambit, White doesn't sacrifice the b pawn. Because of this White has the threat of b5, forcing the knight to move to a5 or e7.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation.
1. e4 e5 2. Nf3 Nc6 3. Bc4 Bc5 4. c3 Nf6 5. d4 exd4 6. b4
| 6 | 7 | 8 | 9 |
| ... Bb6 | e5 d5 | exf6 dxc4 | Qe2+ Be6 | = |
| ... Bb6 | e5 Ne4 | Bd5 Nxc3 | Nxc3 dxc3 | = |
| ... Be7 | e5 Ne4 | b5 Na5 | Bd3 Nxc3 | = |
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# Giuoco Piano: Giuoco Pianissimo Variation

This is the most popular variation. White opens up the bishop and defends e4. Often the light-squared bishop will move to b3, the knight on b1 to d2, the dark-squared bishop to e3 or g5, and kingside castling. Sometimes a4 and b4 thrusts occur as well.
The most popular continuation, 5...d6, is a common move in the Giuoco Piano, with similar reasons as white's 5. d3. The light-squared bishop often comes to e6 or g4, the dark-squared bishop either stays on c5 or sometimes goes to b6 or a7, and Black castles kingside. If Black attempts h6 and g5, she usually castles queenside. Often a6 is played.
5...a6 is also similar and often transposes to main lines of the Pianissimo. The same goes for 5...O-O and 5...h6.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation.
1. e4 e5 2. Nf3 Nc6 3. Bc4 Bc5 4. c3 Nf6
| | 5 | 6 | 7 | 8 |
| Giuoco Pianissimo | ... d6 | O-O a6 | Bb3 Ba7 | Nbd2 O-O | = |
| Giuoco Pianissimo | ... a6 | Bb3 d6 | O-O ... | ... ... | = |
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# Center Attack
## 5...exd4

Black takes the d4 pawn with the e5 pawn. White will respond by either pushing the e4 pawn forward to e5 attacking the Black knight, or recapture the pawn with 6. cxd4

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation.
1. e4 e5 2. Nf3 Nc6 3. Bc4 Bc5 4. c3 Nf6 5. d4 exd4
| | 6 | 7 | 8 | 9 |
| | cxd4 Bb4+ | Bd2 Nxe4 | Bxb4 Nxb4 | Bxf7+ Kxf7 | = |
| | e5 d5 | Bb5 Ne4 | cxd4 Bb4+ | Bd2 Nxd2 | = |
| Walbrodt-Baird Gambit | O-O Nxe4 | cxd4 d5 | dxc5 dxc4 | Qe2 Qd3 | =/+ |
| Dubov Gambit | b4 Bb6 | e5 d5 | exf6 dxc4 | Qe2+ Be6 | = |
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# Dubov Gambit
## 6...Bb6

Black retreats his bishop to b6. White cannot recapture the d4 pawn without hanging the b4 pawn. White can continue with 7. e5.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation.
1. e4 e5 2. Nf3 Nc6 3. Bc4 Bc5 4. c3 Nf6 5. d4 exd4 6. b4 Bb6
| 7 | 8 | 9 | 10 |
| e5 d5 | exf6 dxc4 | Qe2+ Be6 | b5 Na5 | = |
| e5 d5 | exf6 dxc4 | Qe2+ Be6 | b5 d3 | = |
| b5 Na5 | Bd3 d5 | e5 Ne4 | cxd4 O-O | =/+ |
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