hackintoshrao

---
version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:3.2.1
    ports:
     - 32181:32181
    environment:
      ZOOKEEPER_CLIENT_PORT: 32181
      ZOOKEEPER_TICK_TIME: 2000

hackintoshrao

from keras import optimizers

model = Sequential()
model.add(Dense(64, kernel_initializer='uniform', input_shape=(10,)))
model.add(Activation('tanh'))
model.add(Activation('softmax'))

sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)

hackintoshrao

def parameter_optimize(x1, x2, y, w1=w1,w2=w2, b=b, learning_rate = learning_rate):
    # X contains the 100 student records.
    # Iterate through each record.
    for i in range(len(x1)):
        # Make prediction using the initial values of w1, w2, b.
        y_hat = find_perceptron_prediction(x1[i], x2[i], w1, w2, b) 
        # Case where the red points are wrongly classified.
        # This is the case where the actual output is 0 but the prediction is 1.
        if y[i] != y_hat and y[i] == 0:
            # Reduce the values of parameters.

hackintoshrao

def parameter_optimize(x1, x2, y, w1=w1,w2=w2, b=b, learning_rate = learning_rate):
    # X contains the 100 student records.
    # Iterate through each record.
    for i in range(len(x1)):
        # Make prediction using the initial values of W[0], W[1], b.
        y_hat = find_perceptron_prediction(x1[i], x2[i], w1, w2, b) 
        # Case where the red points are wrongly classified.
        # This is the case where the actual output is 0 but the prediction is 1.
        if y[i] != y_hat and y[i] == 0:
            w1 = w1 - test_scores[i] * learning_rate

hackintoshrao

def parameter_optimize(x1, x2, y, w1=w1, w2=w2, b=b, learning_rate=learning_rate):
    # X contains the 100 student records.
    # Iterate through each record.
    for i in range(len(x1)):
        # Make prediction using the initial values of w1, w2, b.
        y_hat = find_perceptron_prediction(x1[i], x2[i], w1, w2, b) 
        # Case where the green points are wrongly classified.
        # This is the case where the actual output is 1 but the prediction is 0.
        if y[i] != y_hat and y[i] == 1:
            # slowly increase the values of the parameters.

hackintoshrao

def parameter_optimize(x1, x2, y, w1=w1, w2=w2, b=b, learning_rate=learning_rate):
    # X contains the 100 student records.
    # Iterate through each record.
    for i in range(len(x1)):
        # Make prediction using the initial values of w1, w2, b.
        y_hat = find_perceptron_prediction(x1[i], x2[i], w1, w2, b) 
        # Case where the red points are wrongly classified.
        # This is the case where the actual output is 0 but the prediction is 1.
        if y[i] != y_hat and y[i] == 0:
        # slowly reduce the parameter values to move the line towards red cluster.

hackintoshrao

from collections import OrderedDict
import numpy as np


# Setting the values for parameters w1, w2 and b.
# Try the code with various parameter values.
w1 = 3
w2 = 2
b =  -1

hackintoshrao

%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np

# extract data frame columns into numpy array.
test_scores = data["test_scores"].values
grades = data["grades"].values
label = data["accepted"].values

hackintoshrao

def processRequest(req):
    # Parsing the POST request body into a dictionary for easy access. 
    req_dict = json.loads(request.data)
    entity_type = ""
    entity_value = ""
    speech = ""
    # Accessing the fields on the POST request boduy of API.ai invocation of the webhook
    intent = req_dict["result"]["metadata"]["intentName"]

    entity_key_val = req_dict["result"]["parameters"]

hackintoshrao

# -*- coding:utf8 -*-
# !/usr/bin/env python
# Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#