Simulación de máquina tragaperras que nos permitirá trabajar con los alumnos de 3er ciclo de primaria conceptos de ESTADÍSTICA tales como la probabilidad, la media, la moda.... Para PONER EN FUNCIONAMIENTO LA MÁQUINA, basta con PULSAR SOBRE LA BANDERA VERDE Para PONER A CERO LA TABLA DE RESULTADOS, basta con PULSAR INTRO También se puede aprovechar para que vean las pocas posibilidades de ganar que tienen los juegos de 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Simulación de máquina tragaperras que nos permitirá trabajar con los alumnos de 3er ciclo de primaria conceptos de ESTADÍSTICA tales como la probabilidad, la media, la moda.... Para PONER EN FUNCIONAMIENTO LA MÁQUINA, basta con PULSAR SOBRE LA BANDERA VERDE Para PONER A CERO LA TABLA DE RESULTADOS, basta con PULSAR INTRO También se puede aprovechar para que vean las pocas posibilidades de ganar que tienen los juegos de azar.

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere ca:_ sense la primera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra ca:_ sense la primera paraula

All but first of empty sentence.

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere ca:_ sense la darrera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra ca:_ sense la darrera paraula

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra de _ 0

pt:a última palavra de _ ca:darrera paraula de _

Last of empty sentence.

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia ca:paraula buida? _ 0

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia ca:frase buida? _

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1

1bf

Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ (

)

Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _

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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==23123123costumes,soundssoundsLimónLimónLimón