acharles7

Key Name                                                Description
========                                                ===========
3GProximityCapability                                   Whether the device has a 3G proximity sensor
3GVeniceCapability                                      Whether the device supports FaceTime over cellular
720pPlaybackCapability                                  Whether the device supports 720p video (identical to kMGQDeviceSupports720p)
APNCapability                                           
ARM64ExecutionCapability                                Whether the device supports executing arm64 binaries
ARMV6ExecutionCapability                                Whether the device supports executing armv6 binaries
ARMV7ExecutionCapability                                Whether the device supports executing armv7 binaries
ARMV7SExecutionCapability                               Whether the device supports executing armv7s binaries

acharles7

{
  "citation": "@article{raecompressive2019,\nauthor = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and\n          Hillier, Chloe and Lillicrap, Timothy P},\ntitle = {Compressive Transformers for Long-Range Sequence Modelling},\njournal = {arXiv preprint},\nurl = {https://arxiv.org/abs/1911.05507},\nyear = {2019},\n}",
  "description": "This dataset contains the PG-19 language modeling benchmark. It includes a set\nof books extracted from the Project Gutenberg books project\n(https://www.gutenberg.org), that were published before 1919. It also contains\nmetadata of book titles and publication dates.\nPG-19 is over double the size of the Billion Word benchmark and contains\ndocuments that are 20X longer, on average, than the WikiText long-range\nlanguage modelling benchmark.\n\nBooks are partitioned into a train, validation, and test set. Books metadata is\nstored in metadata.csv which contains\n(book_id, short_book_title, publication_date, book_link).",
  "location": {
    "urls": [
      "http

acharles7

{
  "citation": "@misc{Dua:2019 ,\nauthor = \"Janosi, Steinbrunn and Pfisterer, Detrano\",\nyear = \"1988\",\ntitle = \"{UCI} Machine Learning Repository\",\nurl = \"http://archive.ics.uci.edu/ml/datasets/Heart+Disease\",\ninstitution = \"University of California, Irvine, School of Information and Computer Sciences\"\n}",
  "description": "This data set contain 13 attributes and labels of heart disease from 303 participants from Cleveland since Cleveland data was most commonlyused in modern research.\n\nAttribute by column index\n1. age      : age in years\n2. sex      : sex (1 = male; 0 = female)\n3. cp       : chest pain type\n    (1 = typical angina; 2 = atypical angina; 3 = non-anginal pain; 4 = asymptomatic)\n4. trestbps : resting blood pressure (in mm Hg on admission to the hospital)\n5. chol     : serum cholestoral in mg/dl\n6. fbs      : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n7. restecg  : resting electrocardiographic results\n8. thalach  : maximum heart rate achieved\n9. exang    : 

acharles7

import numpy as np
import matplotlib.pyplot as plt

# generate random data and plot them
data = np.random.random_integers(10, 20, 100)
num, count = np.unique(data, return_counts=True)
plt.plot(num, count)

each_sample_size = 10
sample_size = 500

acharles7

from skimage.color import rgb2gray
import matplotlib.pyplot as plt

img = plt.imread('cat.jpg')
gray = rgb2gray(img)

gray_r = gray.reshape(gray.shape[0]*gray.shape[1])
global_threshold = gray_r.mean()

for i in range(gray_r.shape[0]):

acharles7

# dataset [n x d]

X = np.array(data_set)
Y = np.array([[93,  7], [50, 34], [51, 79]])

def l2-Distance(X, MU):
	x = np.sum(X**2, axis=1, keepdims=True)
    y = np.sum(MU**2, axis=1)
    xy = np.dot(X, MU.T)
   

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# x = np.random.rand(10,2) * 10
# y = np.random.rand(10,1) * 10

def calculate_entropy(pi):    
    total = 0
    for p in pi:
        p = p / sum(pi)
        if p != 0:
            total += p * np.log2(p)
        else:

acharles7

# x = np.random.rand(10,2) * 10
# y = np.random.rand(10,1) * 10

def calculate_entropy(pi):
    total = 0
    for p in pi:
        p = p / sum(pi)
        if p != 0:
            total += p * np.log2(p)
        else:

acharles7

import numpy as np

w1 = 0.01 * np.random.randn(1, 3)
b1 = np.zeros((1,3))
w2 = 0.01 * np.random.randn(1, 4)
b2 = np.zeros((1,4))
w3 = 0.01 * np.random.randn(1, 1)

f = lambda x: 1/(1 + np.exp(-1)) #Sigmoid Function