KenoLeon

<!-- FILE: App.vue -->
<!-- NOTE: Import bootstrap on main.js  -->

<template>
  <div id="app">
    <bootsCard v-bind="{ cardT : cardTitle, alertD:alertData }"/>
  </div>
</template>

<script>

KenoLeon

# Notes:
# You need to install winsound:
# > pip install winsound
# Only works with windows,
# for an alternative see following gist.

import winsound
#  HZ  and milliseconds
winsound.Beep(200, 1000)

KenoLeon

# Notes:
# Import and install numpy and simpleaudio
# Volume is louder than winsound

import numpy as np
import simpleaudio as sa

def sound(freq,sec):
 frequency = freq
 fs = 44100 #samples per second

KenoLeon

import winsound

#  HZ  and milliseconds
winsound.Beep(40, 1000)
winsound.Beep(80, 1000)
winsound.Beep(160, 1000)
winsound.Beep(320, 1000)
winsound.Beep(640, 1000)
winsound.Beep(1280, 1000)
winsound.Beep(2560, 1000)

KenoLeon

import winsound
#  HZ  and milliseconds
winsound.Beep(262, 600) # C4
winsound.Beep(294, 600) # D4
winsound.Beep(330, 600) # E4
winsound.Beep(349, 600) # F4
winsound.Beep(392, 600) # G4
winsound.Beep(440, 600) # A4
winsound.Beep(494, 600) # B4
winsound.Beep(523, 600) # C3

KenoLeon

import pandas as pd

# CONSTANTS:
ROUND = '220'
TOURNAMENT_NAME = "kazutsugi"
TARGET_NAME = f"target_{TOURNAMENT_NAME}"
PREDICTION_NAME = f"prediction_{TOURNAMENT_NAME}"


# LOAD DATASETS:

KenoLeon

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

def build_model(learning_rate, layer_size):
    """Build Keras model"""
    model = keras.Sequential([
      layers.Dense(layer_size, activation='relu',
                   input_shape=[len(feature_names)]),
      layers.Dense(layer_size, activation='relu', kernel_regularizer='l2'),

KenoLeon

def train_model(model, feature, label, epochs, batch_size):
    """Train Keras Model"""
    history = model.fit(x=feature,
                        y=label,
                        batch_size=batch_size,
                        epochs=epochs)
    epochs = history.epoch
    hist = pd.DataFrame(history.history)
    mse = hist["mse"]

KenoLeon

from matplotlib import pyplot as plt

def plot_the_loss_curve(epochs, mse):
    """Plot a curve of loss vs. epoch."""

    plt.figure()
    plt.xlabel("Epoch")
    plt.ylabel("Mean Squared Error")

    plt.plot(epochs, mse, label="Loss")

KenoLeon

tournament_data[PREDICTION_NAME] = regressor_model.predict(tournament_data[feature_names])
df = tournament_data[PREDICTION_NAME]
df.columns = ["id", "prediction_kazutsugi"]
df.to_csv("Numerai/" + TOURNAMENT_NAME + "_submission_YourSubmissionName.csv", header=True)
print(df)