Mikko Kotila mikkokotila

you like computers, I like computers

mikkokotila / coronacaster_experiment_code.py

Last active October 24, 2020 14:00

	import datetime, coronacaster, pandas

	data = coronacaster.get_data_from_eu()

	out = []
	countries = data.countries
	for country in countries:

	# parameters start
	for ftype in ['poly1', 'poly2', 'poly3', 'logis', 'sigmoid', 'scurve']:

mikkokotila / create_datatables_from_csv.js

Created August 4, 2019 21:02

	function csvImport(path_to_file) {

	d3.csv(path_to_file).then(prepData);

	function prepData(dataset) {

	// create the columns

	var keys = Object.keys(dataset[0])
	var headers = []

mikkokotila / create_python_service_systemd.sh

Last active April 14, 2019 11:42

	# name of the service
	NAME=$1

	# exec and service file paths
	EXEC_FILE=$2
	SERVICE_FILE=/etc/systemd/system/"$NAME".service

	# timing
	TIMER_FILE=/etc/systemd/system/"$NAME".timer
	TIMING=$3

mikkokotila / cervical_cancer_assets.py

Created March 7, 2019 15:56

	import talos as ta

	x, y = ta.templates.datasets.cervical_cancer()
	p = ta.templates.params.cervical_cancer()
	model = ta.templates.models.cervical_cancer()

mikkokotila / signs_minimal_example.py

Created February 21, 2019 12:49

	import signs as signs
	import pandas as pd

	# load some text
	df = pd.read_csv('tweets.csv').text

	# load vectors
	e = signs.Embeds("glove.twitter.27B.25d.txt")

	# get Keras embeddings layer

mikkokotila / talos_test.py

Last active January 11, 2019 11:22

	import talos as ta
	from keras.models import Sequential
	from keras.layers import Dense

	def minimal():

	x, y = ta.datasets.iris()

	p = {'activation':['relu', 'elu'],
	'optimizer': ['Nadam', 'Adam'],

mikkokotila / minimal_talos_example.py

Last active November 21, 2020 12:19

	import talos
	from keras.models import Sequential
	from keras.layers import Dense

	def minimal():

	x, y = talos.templates.datasets.iris()

	p = {'activation':['relu', 'elu'],
	'optimizer': ['Nadam', 'Adam'],

mikkokotila / minimal_keras.py

Created January 6, 2019 18:35

	def iris_model(x_train, y_train, x_val, y_val):

	model = Sequential()
	model.add(Dense(32, input_dim=8, activation='adam'))
	model.add(Dense(1, activation='sigmoid'))
	model.compile(optimizer='relu', loss='binary_crossentropy')

	out = model.fit(x_train, y_train,
	batch_size=24,
	epochs=100,

mikkokotila / breast_cancer_params_b.py

Created May 12, 2018 18:04

	p = {'lr': (0.8, 1.2, 3),
	'first_neuron':[4, 8, 16, 32, 64],
	'hidden_layers':[0, 1, 2],
	'batch_size': (1, 5, 5),
	'epochs': [50, 100, 150],
	'dropout': (0, 0.2, 3),
	'weight_regulizer':[None],
	'emb_output_dims': [None],
	'shape':['brick','long_funnel'],
	'kernel_initializer': ['uniform','normal'],

mikkokotila / breast_cancer_run.py

Created May 12, 2018 09:12