How to Be Anti-Social in Augmented Reality

Augmented Reality Heads Up Display

Communicate anonymously through an augmented reality heads up display for Android only. Download app from Google Play.

---

Python Machine Learning on Amazon stock prices

This Python code reads Amazon's historical stock prices from 2014 to 2019. I downloaded the CSV file from Yahoo Finance. The chart below shows how well this algorithm predicts stocks prices when compared to actual stock prices. The code was cobbled together from snippets at Analytics Vidhya and Medium.

# importing libraries

import pandas as pd

import numpy as np

from datetime import date, datetime

import calendar

#importing required libraries

from sklearn.preprocessing import MinMaxScaler

from keras.models import Sequential

from keras.layers import Dense, Dropout, LSTM

# reading the data
df = pd.read_csv('amzn2.csv')


# looking at the first five rows of the data

print('\n Original data:')

print(df.head())

print('\n Shape of original data:')

print(df.shape)


# setting the index as date

df['Date'] = pd.to_datetime(df.Date,format='%Y-%m-%d')

df.index = df['Date']


#creating dataframe

data = df.sort_index(ascending=True, axis=0)

new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date', 'Close'])

#populate new data frame

for i in range(0,len(data)):

    new_data['Date'][i] = data['Date'][i]

    new_data['Close'][i] = data['Close'][i]


#setting index

new_data.index = new_data.Date

new_data.drop('Date', axis=1, inplace=True)


#creating train and test sets

dataset = new_data.values

#the csv file has 1260 records

train = dataset[0:630,:]

valid = dataset[630:,:]


#converting dataset into x_train and y_train

scaler = MinMaxScaler(feature_range=(0, 1))

scaled_data = scaler.fit_transform(dataset)


x_train, y_train = [], []

for i in range(60,len(train)):

    x_train.append(scaled_data[i-60:i,0])

    y_train.append(scaled_data[i,0])

x_train, y_train = np.array(x_train), np.array(y_train)


x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))


# create and fit the LSTM network

model = Sequential()

model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1],1)))

model.add(LSTM(units=50))

model.add(Dense(1))


model.compile(loss='mean_squared_error', optimizer='adam')

model.fit(x_train, y_train, epochs=1, batch_size=1, verbose=2)


#predicting 246 values, using past 60 from the train data

inputs = new_data[len(new_data) - len(valid) - 60:].values

inputs = inputs.reshape(-1,1)

inputs  = scaler.transform(inputs)


X_test = []

for i in range(60,inputs.shape[0]):

    X_test.append(inputs[i-60:i,0])

X_test = np.array(X_test)


X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))

closing_price = model.predict(X_test)

closing_price = scaler.inverse_transform(closing_price)

rms=np.sqrt(np.mean(np.power((valid-closing_price),2)))

print('\n Root Mean Square Deviation:')

print(rms)

#for plotting

#plot

import matplotlib.pyplot as plt

train = new_data[:630]

valid = new_data[630:]

valid['Predictions'] = closing_price

plt.plot(train['Close'])

plt.plot(valid[['Close','Predictions']])

plt.show()

Victory Cross Country 2011 motorcycle stalling

Came to a stop okay, but then accelerated and the bike stalled like it was flooded but it's fuel injection. Rolled to a stop on a downwill slope and reastarted briefly. The engine light was on and it stalled again, never to restart. Battery working as evidenced by lights being on and pump priming. The culprit was a busted and dangling tip over sensor that cuts off the fuel supply when bike is on its side. Since the tip over sensor broke off and was dangling upside down, the bike was going no where fast. Must replace tip over sensor. Found one here.

Python and MySQL on the Kindle Fire HD 6 inch

I got a QPython IDE from here: ww.appsapk.com/qpython-python-for-android

MySQL and PHPMyAdmin came from here: www.apkfiles.com/apk-541908/android-web-server-kickweb-server-5-0

From pip console in the QPython app, I ran "pip install mysql-connector --index-url https://qpypi3.qpython.org/simple/.

At the time that you read this, the links may have changed, but the approach will be the same. Find a QPython apk that you can download from outside of Google Play, see if you can use the PIP console to install AI libraries, adjust the QPypi url in the settings if needed, get a web server app with MySQL and PHP, try to install mysql-connector and finally use the IDE to run data analysis on what ever you import into MySQL and scrub. There is a lot of hit and miss, because of the various apks that may be Chinese or Google versions and neither will work. You must find pure Android apks.

You may also need a fire extinguisher for when the Kindle Fire really does become fire and explodes while running all your clever little machine learning algorithms.

New UI for GPS Time Clock on Google Play

Try out your GPS time clock to keep field therapists honest. GPS Time Clock on Google Play

Python - Read MySQL Table and Format Output

# Using phpadmin to load mysql by importing from some of these sources
# http://www.cboe.com/delayedquote/quote-table
# https://datashop.cboe.com/option-quotes-end-of-day-with-calcs
# https://www.stock-data-solutions.com/download.htm
# https://www.worldtradingdata.com/services
import mysql.connector
optionsdb = mysql.connector.connect(
 host="localhost",
 user="root",
 #passwd=""
 database="CSV_DB"
 )
optionscursor = optionsdb.cursor()
optionscursor.execute("select convert(`expiration`, CHAR) as expiration, `option_type` as `T`,
 convert(`strike`, CHAR) as `strike`, convert(`delta_1545`, CHAR) as `delta`, convert(`vega_1545`,
 CHAR) as `vega`, convert(`theta_1545`, CHAR) as `theta` FROM `GREEKS` where `open_interest` > 50 and
 ABS(`delta_1545`) > .20 and ABS(`theta_1545`) < 1
 order by `expiration` asc, `option_type` asc, `strike` asc limit 100")
optionsresult = optionscursor.fetchall()
colheaders = []
colwidths = []
coldivider = '|'
colseparator = '+'
for names in optionscursor.description:
    colwidths.append(11)
    colheaders.append(names[0])
for w in colwidths: 
    coldivider += " %-"+"%ss |" % (w,)
    colseparator += '-'*w + '--+'
print(colseparator)
print(coldivider % tuple(colheaders))
print(colseparator)
for coldata in optionsresult:
 print(coldivider % coldata)

After running, one may get something that looks like this: