Creating the maze takes a while. Please be paitent. Move the mouse by arrow keys. Try to catch the cheese. I used matrices functions from wunder_wulfe. See https://snap.berkeley.edu/project?user=wunder_wulfe&project=Misc%20Math%20Componentsdata:image/png;base64,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data:image/png;base64,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Objekt(3)Returns a JS object where each pair of inputs is a key-value-pair
For loop that has a step value
1101Retrieve a value from a JS object at a specific key
Casts to a number
Casts to a string
Uses the typeof function from JS
Assign a value to a key in a JS object
Iterate through the key-value-pairs of a JS object
Creates a JS array from inputs
Create an N x N matrix with a specific fill mode
330identityPerform a matrix multiplication
resultsumresult0i1Throw an error to blocks
An error has occurred
Not equal to
Greater than or equal to
Get the amount of rows in a matrix
Get the amount of columns in a matrix
Index a matrix at a given row and column
1last1lastlastReplace the value at a given row and column for a matrix
1last1last0lastGenerate a vector
Create a new CFrame CFrames are composed of a rotation matrix and a positional vector joined into one 4x4 matrix
000Returns a CFrame representing a rotation about the axis
xx y z0§_directionDialMenuConstruct a CFrame using the rotation matrix
100010001Negates a value
0Alias for reportMonadic with cos
0§_directionDialMenuAlias for reportMonadic with sin
0§_directionDialMenu3D Vector
000Perform a unary operation on a vector
xx y z length (magnitude)=length normalized - (negate)=-Set a data member of a vector
xx y z0Make a CFrame from a vector
Get a data member from a CFrame
forwardright up forward position x y zTranslate a CFrame by a vector in either global or local space
trueTransform a vector relative to CFrame into world space
lastCreate a duplicate matrix
Increment or decrement an existing value from a matrix
1last1last0Perform a binary vector operation
∙∙ (dot product)=∙ × (cross product)=× * (multiply)=* ÷ (divide)=÷ + (add)=+ - (subtract)=-Generate a CFrame from A pointing towards B
Move a CFrame to a vector coordinate
Shift a CFrame globally by a vector
Matrix multiplication, optimized for CFrames
Matrix inversion, optimized for CFrames This can be used to convert from World to Object space instead of Object to World
j145lastJoin two matrices side by side
copycopyirowAlternative for the builtin list loop, providing the index
1j111-1Grab a range of columns out of a matrix
1last1lastlastcopyi1Shallow copy of a list
Get the stage's screen resolution
Converts a percentage vector into stage coordinates
00Debug that makes lists print quicker
Similar to map, but with a more general parameter that can accept the index as the input
01Compute the estimated derivative of the function.
0Compute the estimated integral within the region
110000many (slower, more accurate)=10000 moderate (balanced)=1000 few (faster, less accurate)=10003Interpolate from a to b linearly
010Constructs a function with its derivative and antiderivative in order to compute calculus functions without approximations
Whether or not an object is the emty string or an empty ring
Alias for dev mode type of block
Preset functions Only feed in parameters if the function requires one (x^n for example)
sigmoidsigmoid x^n sin cos
0Compute the inverse of a matrix
divisork1j11last
1j111-1Compute the sum of a function
10sum0i1Estimate the limit of a function
0∞ -∞++ -∞+1-1
Compute the product of a function
10sum1i1Generate cframe directly from matrix values
100001000010Convert between pitches and frequencies
69§_pianoKeyboardMenufrequencypitch frequencyTranspose a matrix
OSSWWNNWSO0Per Zufall nächstes Feld finden. ZielGefunden = ausgewähltes Feld ist frei10Zufallszahl_110100101103Wenn nicht in Sackgasse, ...
Loch UND Loch ist größer als ein FeldPer Zufall den Startpunkt finden.Falls neben dem Loch ein Weg liegt, der nicht der Rand ist, dann ...Liste durchgehen per Zufall3036048053