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gagejustins / call.py
Last active Mar 23, 2018
Code for sentiment analysis with Twitter demo
View call.py
#Define your API Key from Algorithmia
apikey = 'YOUR_API_KEY'
#Initialize the Algorithmia client
client = Algorithmia.client(apikey)
#Create an instance of the RetrieveTweetsWithKeyword algorithm
algo = client.algo('diego/RetrieveTweetsWithKeyword/0.1.2')
#Call the algorithm for both of our keywords and store the results
@gagejustins
gagejustins / followers.py
Last active Apr 5, 2018
Code for Twitter algorithm spotlight
View followers.py
import Algorithmia
input = {
"username": "elonmusk",
"auth": {
"app_key": "xxxxxxxxxx",
"app_secret": "xxxxxxxxxx",
"oauth_token": "xxxxxxxxxx",
"oauth_token_secret": "xxxxxxxxxx"
}
View optimizer.py
#Import the optim module from the pytorch package
import torch.optim as optim
#Initialize an optimizer object
learning_rate = 0.001
optimizer = optim.Adam(net.parameters(), lr=learning_rate)
#Set the parameter gradients to 0 and take a step (as part of a training loop)
for epoch in num_epochs:
train(...)
View checkpoint.py
for epoch in range(numEpochs):
train(...) #Training code
accuracy = eval(...) #Evaluate accuracy
#If we've reached a new best accuracy
if accuracy > best_accuracy:
#Save model checkpoint
torch.save({'epoch': epoch,
'state_dict': model.state_dict(),
View slash.js
const request = require('request');
/**
* Sends input from a Slack SlashCommand to Algorithmia's nlp/SummarizeURL, and returns result
*
* @param {!Object} req Cloud Function request context.
* @param {!Object} res Cloud Function response context.
*/
exports.summarizeURL = function summarizeURL(req, res) {
// verify that this request came from a valid Slack App
View jordans.tsv
sale_date chicago white blue
9/7/17 2899
9/8/17 2645
9/9/17 2510.5
9/10/17 2412.5
9/11/17 2509.5
9/12/17 2391.4
9/13/17 2104
9/14/17 2002.214286
9/15/17 1955.3125
View chicago_daily.csv
sale_date sale_price
2017-09-07 2899.0
2017-09-08 2645.0
2017-09-09 2510.5
2017-09-10 2412.5
2017-09-11 2509.5
2017-09-12 2391.4
2017-09-13 2104.0
2017-09-14 2002.2142857142858
2017-09-15 1955.3125
View chicago_weekly.csv
sale_date sale_price
2017-08-20 1800.0
2017-08-27 2100.0
2017-09-03 2100.0
2017-09-10 2547.6666666666665
2017-09-17 2042.0597014925372
2017-09-24 1870.6041666666667
2017-10-01 1880.4285714285713
2017-10-08 1885.357142857143
2017-10-15 1869.68
View jordans_weekly.csv
sale_date white blue chicago
8/20/17 1800
8/27/17 2100
9/3/17 2100
9/10/17 2547.666667
9/17/17 2042.059701
9/24/17 1870.604167
10/1/17 1880.428571
10/8/17 1885.357143
10/15/17 1869.68
View af1.csv
sale_date sale_price
2017-10-22 1500.0
2017-10-29 790.1212121212121
2017-11-05 826.0869565217391
2017-11-12 858.8181818181819
2017-11-19 597.9185185185186
2017-11-26 498.4469696969697
2017-12-03 490.2369668246445
2017-12-10 513.1603053435115
2017-12-17 515.9662921348314
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