vzhou842

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.calibration import CalibratedClassifierCV
from sklearn.svm import LinearSVC
from sklearn.externals import joblib

# Read in data
data = pd.read_csv('clean_data.csv')
texts = data['text'].astype(str)
y = data['is_offensive']

vzhou842

(function() {
  (function() {
    console.log('Hello')
  })()

  (function() {
    console.log('World!')
  })()
})()

vzhou842

(function() {
  (function() {
    console.log('Hello')
  })();

  (function() {
    console.log('World!')
  })()
})()

vzhou842

const f1 = function() { console.log('Hello'); };
const f2 = function() { console.log('World!'); };

f1()(f2)();

vzhou842

const f1 = function() { console.log('Hello'); };
const f2 = function() { console.log('World!'); };

f1();(f2)();

vzhou842

const a = 'Hello'
const b = 'World' + '!'
[a, b].forEach(s => console.log(s))

vzhou842

import numpy as np

def sigmoid(x):
  # Our activation function: f(x) = 1 / (1 + e^(-x))
  return 1 / (1 + np.exp(-x))

class Neuron:
  def __init__(self, weights, bias):
    self.weights = weights
    self.bias = bias

vzhou842

import numpy as np

# ... code from previous section here

class OurNeuralNetwork:
  '''
  A neural network with:
    - 2 inputs
    - a hidden layer with 2 neurons (h1, h2)
    - an output layer with 1 neuron (o1)

vzhou842

import numpy as np

def mse_loss(y_true, y_pred):
  # y_true and y_pred are numpy arrays of the same length.
  return ((y_true - y_pred) ** 2).mean()

y_true = np.array([1, 0, 0, 1])
y_pred = np.array([0, 0, 0, 0])

print(mse_loss(y_true, y_pred)) # 0.5

vzhou842

import numpy as np

def sigmoid(x):
  # Sigmoid activation function: f(x) = 1 / (1 + e^(-x))
  return 1 / (1 + np.exp(-x))

def deriv_sigmoid(x):
  # Derivative of sigmoid: f'(x) = f(x) * (1 - f(x))
  fx = sigmoid(x)
  return fx * (1 - fx)