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X <- numeric(length=1000)
params.a <- sqrt(30) / 4
params.c <- 0.811

sqrt.fx <- function(x) {
  f <- (x ^ 4) - 2 * (x ^ 3) + (x ^ 2)
  return(sqrt(30 * f))
}

sample <- function() {

jaidevd

Suppose you have $n$ random variables, $\mathbf{x}_1, \mathbf{x}_2, x_3, \ldots x_n $.

Now, between any two random variables $x_i, x_j$ the covariance is defined as:

$$ \sigma_{\mathbf{x}_i, \mathbf{x}_j} = E[(\mathbf{x}i - \mu{\mathbf{x}_i})(\mathbf{x}j - \mu{\mathbf{x}_j})] $$

(where $\mu_{\mathbf{x}_i}$ is the mean of $\mathbf{x}_i$).

jaidevd

from flask import request, Flask

app = Flask(__name__)


@app.route("/", methods=["POST"])
def index():
    if request.content_type == 'application/pdf':
        with open('request.pdf', 'wb') as fout:
            fout.write(request.data)

jaidevd

# coding: utf-8

from keras import layers as L
from keras import backend as K
from keras.models import Model
import tensorflow as tf
import numpy as np

_epsilon = tf.convert_to_tensor(K.epsilon(), np.float32)

jaidevd

Generative vs discriminative classifiers

Discriminative classifiers:

Discriminative classifiers divide the feature space into regions that separate the data belonging to different classes such that every separate region contains samples belonging to a single class. The decision boundary is determined by constructing and solving equations of the form

$$ \mathbf{y} = \mathbf{w}^T\mathbf{\phi(X)}+b $$

jaidevd

[
  {
    "text": "#BREAKINGNEWS MALAYSIA AIRLINES FLIGHT #MH17 CONFIRMED SHOT DOWN OVER #DONETSK OBLAST, SHORTLY BEFORE REACHING RUSSIAN AIR SPACE",
    "labels": [
      {
        "start": 14,
        "end": 31,
        "label": "ORG"
      },
      {

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[{"text":"For the last quarter of 2010 , Componenta 's net sales doubled to EUR131m from EUR76m for the same period a year earlier , while it moved to a zero pre-tax profit from a pre-tax loss of EUR7m .","label":"POSITIVE"},{"text":"$FB gone green on day","label":"POSITIVE"},{"text":"$MSFT SQL Server revenue grew double-digit with SQL Server Premium revenue growing over 30% http:\/\/stks.co\/ir2F","label":"POSITIVE"},{"text":"Costco: A Premier Retail Dividend Play https:\/\/t.co\/Fa5cnh2t0t $COST","label":"POSITIVE"},{"text":"Stockmann and Swedish sector company AB Lindex entered into an agreement on September 30 , 2007 , whereby Stockmann , or a wholly-owned subsidiary of it , will make a public tender offer for all of Lindex 's issued shares .","label":"POSITIVE"},{"text":"The item included restructuring costs of EUR1 .6 m , while a year earlier they were EUR13 .1 m. Diluted EPS stood at EUR0 .3 versus a loss per share of EUR 0.1 .","label":"POSITIVE"},{"text":"A portion , $ 12.5 million , will be recorded

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import numpy as np
from sklearn.model_selection import train_test_split


def add_dummy_feature(x):
    return np.column_stack((np.ones(x.shape[0]), x))


# Predicting label follows the equation y = Xw, in its vectorized form.
# def predict(X, w):

jaidevd

url:
  home:
    pattern: /$YAMLURL/
    handler: FunctionHandler
    kwargs:
      function: img.do
      headers:
        Content-Type: image/png