thomashikaru

# sent_id = answers-20111108084416AAoPgBv_ans-0020
# text = Everything about the place is magical and the people are mostly friendly.
1	Everything	everything	PRON	NN	Number=Sing	6	nsubj	6:nsubj	_
2	about	about	ADP	IN	_	4	case	4:case	_
3	the	the	DET	DT	Definite=Def|PronType=Art	4	det	4:det	_
4	place	place	NOUN	NN	Number=Sing	1	nmod	1:nmod	_
5	is	be	AUX	VBZ	Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin	6	cop	6:cop	_
6	magical	magical	ADJ	JJ	Degree=Pos	0	root	0:root	_
7	and	and	CCONJ	CC	_	12	cc	12:cc	_
8	the	the	DET	DT	Definite=Def|PronType=Art	9	det	9:det	_

thomashikaru

import pygame
import numpy as np
import itertools
import sys
import networkx as nx
import collections
from pygame import gfxdraw


# Game constants

thomashikaru

# sent_id = 1288710
# text = Возможно, это ощущение было еще сильнее, чем удовлетворение от самого успеха.
1	Возможно	возможно	ADV	_	Degree=Pos	7	parataxis	_	SpaceAfter=No
2	,	,	PUNCT	_	_	1	punct	_	_
3	это	этот	DET	_	Case=Nom|Gender=Neut|Number=Sing	4	amod	_	_
4	ощущение	ощущение	NOUN	_	Animacy=Inan|Case=Nom|Gender=Neut|Number=Sing	7	nsubj	_	_
5	было	быть	AUX	_	Aspect=Imp|Gender=Neut|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act	7	cop	_	_
6	еще	еще	ADV	_	Degree=Pos	7	advmod	_	_
7	сильнее	сильный	ADJ	_	Degree=Cmp	0	root	_	SpaceAfter=No
8	,	,	PUNCT	_	_	7	punct	_	_

thomashikaru

from collections import defaultdict

d = defaultdict(int)
print(d["Alice"]) # prints 0

d["Bob"] += 1
print(d["Bob"]) # prints 1 

d = defaultdict(list)
d["John"].append("eggs")

thomashikaru

from collections import defaultdict


class BigramModel:
    def train(self, training_set):
        self.d = defaultdict(lambda: defaultdict(int))
        for sent in training_set:
            for w1, w2 in zip(sent[:-1], sent[1:]):
                self.d[w1][w2] += 1

thomashikaru

from collections import Counter
import glob

# get list of filenames matching a pattern using glob
filenames = glob.glob("path/to/many/files/*.txt")

# create empty counter object
counts = Counter()

# loop over files, create a counter for each, and merge into counts

thomashikaru

# without formatter
dictionary = {"a":[1,2,3,4,5,6,7,8,9,8,7,6,5,4,3,2,1], "b":[9,8,7,6,5,4,3,2,1,2,3,4,5,6,7,8,9]}
list_of_items = [f"A: {a}, B: {b}, C: {c}" for a, b, c in itertools.product(range(0,100,2), range(0,100,3), range(0,100,4))]

# with formatter
dictionary = {
    "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 7, 6, 5, 4, 3, 2, 1],
    "b": [9, 8, 7, 6, 5, 4, 3, 2, 1, 2, 3, 4, 5, 6, 7, 8, 9],
}
list_of_items = [

thomashikaru

import argparse
import pandas as pd

if __name__ == "__main__":
  
  # create argument parser and define arguments
  parser = argparse.ArgumentParser()
  parser.add_argument("--inputfile", default="inputfile.csv")
  parser.add_argument("--num_rows", type=int, default=10)
  parser.add_argument("--print_output", action="store_true")

thomashikaru

import torch
import plotly.graph_objects as go
import numpy as np

# Batch Size, Input Neurons, Hidden Neurons, Output Neurons
N, D_in, H, D_out = 16, 1, 1024, 1

# Create random Tensors to hold inputs and outputs
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)

thomashikaru

import torch
import plotly.express as px
import pandas as pd

# Batch Size, Input Neurons, Hidden Neurons, Output Neurons
N, D_in, H, D_out = 128, 2, 1024, 1

# Create random Tensors to hold inputs and outputs
x = torch.rand(N, D_in)
y = torch.randint(0, 2, (N, D_out))