def load_dictionary(self, words): for word in words: code = self.encode(word) if code not in self.word_dict: self.word_dict[code] = [] self.word_dict[code].append(word)
def input_digit(self, digit): if digit == '0': # Space - finalize current word if self.current_input and self.current_input in self.word_dict: self.output_text += self.word_dict[self.current_input][0] + " " self.current_input = "" else: self.current_input += digit if self.current_input in self.word_dict: return self.word_dict[self.current_input] return []
nextPrediction() // Cycle through predictions if (this.predictions.length > 1) this.predictions.push(this.predictions.shift()); return this.predictions[0];
loadDictionary(words) words.forEach(word => const seq = this.wordToSequence(word); if (!this.dictionary[seq]) this.dictionary[seq] = []; this.dictionary[seq].push(word); );
class SmartT9: def __init__(self): self.word_frequency = {} def get_predictions(self, sequence): words = self.dictionary.get(sequence, []) return sorted(words, key=lambda w: self.word_frequency.get(w, 0), reverse=True)
Store common words mapped to their T9 sequences:
}
pressKey(key)
# Example word dictionary t9_dict = '4663': ['good', 'home', 'gone'], '2273': ['case', 'care', 'base'], '96753': ['words', 'world'], '43556': ['hello'], '843': ['the', 'tie', 'vid']
const starterDictionary = '2': ['a', 'b', 'c'], '22': ['aa', 'ab', 'ac', 'ba', 'bb', 'bc', 'ca', 'cb', 'cc'], '23': ['ad', 'ae', 'af', 'bd', 'be', 'bf', 'cd', 'ce', 'cf'], '4663': ['good', 'home', 'gone', 'hood'], '43556': ['hello'], '96753': ['world', 'words'], '843': ['the', 'tie', 'vid'], '2865': ['bunk', 'cunt', 'auto'], '5464': ['king', 'link', 'jink'], '7364': ['send', 'rend', 'pend'] ; 1. Next Word Prediction Allow cycling through predictions with a "Next" key (usually * ) 2. Add Word to Dictionary Let users add new words that aren't recognized 3. Frequency-Based Sorting Sort predictions by how often the user selects them
acceptWord() // Add to output, reset sequence console.log('Word accepted:', this.predictions[0]
wordToSequence(word) return word.toLowerCase().split('').map(ch => for (let [key, letters] of Object.entries(this.keyMap)) if (letters.includes(ch)) return key; return ch === ' ' ? '0' : ''; ).join('');
What is T9? T9 (Text on 9 keys) is a predictive text technology from the late 1990s/early 2000s that allowed users to type on mobile phones with 9 number keys (2-9). Each key maps to multiple letters, and T9 predicts the intended word based on key sequences. Key Mapping Key 2: ABC Key 3: DEF Key 4: GHI Key 5: JKL Key 6: MNO Key 7: PQRS Key 8: TUV Key 9: WXYZ Key 0: Space Key 1: Punctuation (varies by implementation) How to Build a T9 Emulator Step 1: Create the Letter-to-Key Mapping # Python example letter_to_key = 'a': '2', 'b': '2', 'c': '2', 'd': '3', 'e': '3', 'f': '3', 'g': '4', 'h': '4', 'i': '4', 'j': '5', 'k': '5', 'l': '5', 'm': '6', 'n': '6', 'o': '6', 'p': '7', 'q': '7', 'r': '7', 's': '7', 't': '8', 'u': '8', 'v': '8', 'w': '9', 'x': '9', 'y': '9', 'z': '9', ' ': '0'
multi_tap = '2': ['a', 'b', 'c'], '3': ['d', 'e', 'f'], '4': ['g', 'h', 'i'], '5': ['j', 'k', 'l'], '6': ['m', 'n', 'o'], '7': ['p', 'q', 'r', 's'], '8': ['t', 'u', 'v'], '9': ['w', 'x', 'y', 'z']
def load_dictionary(self, words): for word in words: code = self.encode(word) if code not in self.word_dict: self.word_dict[code] = [] self.word_dict[code].append(word)
def input_digit(self, digit): if digit == '0': # Space - finalize current word if self.current_input and self.current_input in self.word_dict: self.output_text += self.word_dict[self.current_input][0] + " " self.current_input = "" else: self.current_input += digit if self.current_input in self.word_dict: return self.word_dict[self.current_input] return []
nextPrediction() // Cycle through predictions if (this.predictions.length > 1) this.predictions.push(this.predictions.shift()); return this.predictions[0];
loadDictionary(words) words.forEach(word => const seq = this.wordToSequence(word); if (!this.dictionary[seq]) this.dictionary[seq] = []; this.dictionary[seq].push(word); ); t9 keyboard emulator
class SmartT9: def __init__(self): self.word_frequency = {} def get_predictions(self, sequence): words = self.dictionary.get(sequence, []) return sorted(words, key=lambda w: self.word_frequency.get(w, 0), reverse=True)
Store common words mapped to their T9 sequences:
}
pressKey(key)
# Example word dictionary t9_dict = '4663': ['good', 'home', 'gone'], '2273': ['case', 'care', 'base'], '96753': ['words', 'world'], '43556': ['hello'], '843': ['the', 'tie', 'vid']
const starterDictionary = '2': ['a', 'b', 'c'], '22': ['aa', 'ab', 'ac', 'ba', 'bb', 'bc', 'ca', 'cb', 'cc'], '23': ['ad', 'ae', 'af', 'bd', 'be', 'bf', 'cd', 'ce', 'cf'], '4663': ['good', 'home', 'gone', 'hood'], '43556': ['hello'], '96753': ['world', 'words'], '843': ['the', 'tie', 'vid'], '2865': ['bunk', 'cunt', 'auto'], '5464': ['king', 'link', 'jink'], '7364': ['send', 'rend', 'pend'] ; 1. Next Word Prediction Allow cycling through predictions with a "Next" key (usually * ) 2. Add Word to Dictionary Let users add new words that aren't recognized 3. Frequency-Based Sorting Sort predictions by how often the user selects them def load_dictionary(self, words): for word in words: code
acceptWord() // Add to output, reset sequence console.log('Word accepted:', this.predictions[0]
wordToSequence(word) return word.toLowerCase().split('').map(ch => for (let [key, letters] of Object.entries(this.keyMap)) if (letters.includes(ch)) return key; return ch === ' ' ? '0' : ''; ).join('');
What is T9? T9 (Text on 9 keys) is a predictive text technology from the late 1990s/early 2000s that allowed users to type on mobile phones with 9 number keys (2-9). Each key maps to multiple letters, and T9 predicts the intended word based on key sequences. Key Mapping Key 2: ABC Key 3: DEF Key 4: GHI Key 5: JKL Key 6: MNO Key 7: PQRS Key 8: TUV Key 9: WXYZ Key 0: Space Key 1: Punctuation (varies by implementation) How to Build a T9 Emulator Step 1: Create the Letter-to-Key Mapping # Python example letter_to_key = 'a': '2', 'b': '2', 'c': '2', 'd': '3', 'e': '3', 'f': '3', 'g': '4', 'h': '4', 'i': '4', 'j': '5', 'k': '5', 'l': '5', 'm': '6', 'n': '6', 'o': '6', 'p': '7', 'q': '7', 'r': '7', 's': '7', 't': '8', 'u': '8', 'v': '8', 'w': '9', 'x': '9', 'y': '9', 'z': '9', ' ': '0' Frequency-Based Sorting Sort predictions by how often the
multi_tap = '2': ['a', 'b', 'c'], '3': ['d', 'e', 'f'], '4': ['g', 'h', 'i'], '5': ['j', 'k', 'l'], '6': ['m', 'n', 'o'], '7': ['p', 'q', 'r', 's'], '8': ['t', 'u', 'v'], '9': ['w', 'x', 'y', 'z']