thomashikaru

import pandas as pd
import json
import math
import plotly.express as px
import numpy as np
from sklearn import neighbors

# load geojson data for manhattan
nycmap = json.load(open("nycpluto_manhattan.geojson"))

thomashikaru

import pandas as pd
import json
import math
import plotly.express as px
from area import area

# read the neighborhood population data into a DataFrame and load the GeoJSON data
df = pd.read_csv('New_York_City_Population_By_Neighborhood_Tabulation_Areas.csv')
nycmap = json.load(open("nyc_neighborhoods.geojson"))

thomashikaru

import numpy as np


class MarkovModel:
    """Represents a Markov Model for a given text"""

    def __init__(self, n, text):
        """Constructor takes n-gram length and training text
        and builds dictionary mapping n-grams to
        character-probability mappings."""

thomashikaru

import tensorflow as tf
import numpy as np
import os

# load and lightly pre-process data
text = " ".join(open("trump_tweets_all.txt").readlines())
text = " ".join(text.split())
text = text.encode("ascii", errors="ignore").decode()
print(text[:100])

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))

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 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

# 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

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

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