rayheberer

class AtariNet(object):
    # ...
    # ...
    def _build(self):
        # ...
        # ...
        # convolutional layers for minimap features
        self.minimap_conv1 = tf.layers.conv2d(
            inputs=self.minimap_processed,
            filters=16,

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def preprocess_spatial_features(features, screen=True):
    """Embed categorical spatial features, log transform numeric features."""
    # ...
    # ...
    preprocess_ops = []
    for index, (feature_type, scale) in enumerate(feature_specs):
        layer = transposed[:, :, :, index]

        if feature_type == sc2_features.FeatureType.CATEGORICAL:
            # one-hot encode in channel dimension -> 1x1 convolution

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class AtariNet(object):
# ...
# ...
# ...
    def _build(self):
        # ...
        # ...
        self.screen_features = tf.placeholder(
            tf.int32,
            [None, len(SCREEN_FEATURES), *self.screen_dimensions],

rayheberer

 class DQNMoveOnly(base_agent.BaseAgent):
     # ...
     # ...
     # ...
     # ...
     def _update_target_network(self):
        online_vars = tf.get_collection(
            tf.GraphKeys.TRAINABLE_VARIABLES, 'DQN')
        target_vars = tf.get_collection(
            tf.GraphKeys.TRAINABLE_VARIABLES, 'DQNTarget')

rayheberer

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.datasets import load_iris

data = load_iris()
fig, axes = plt.subplots(nrows=2, ncols=2)
fig.subplots_adjust(hspace=0.5)
fig.suptitle('Distributions of Iris Features')

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import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_iris

data = load_iris()
fig, axes = plt.subplots(nrows=2, ncols=2)
fig.subplots_adjust(hspace=0.5)
fig.suptitle('Distributions of Iris Features')

for ax, feature, name in zip(axes.flatten(), data.data.T, data.feature_names):

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import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV

X, y = load_iris(return_X_y=True)

np.random.seed(41)

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from utils import *
import ast

row_units = [cross(r, cols) for r in rows]
column_units = [cross(rows, c) for c in cols]
square_units = [cross(rs, cs) for rs in ('ABC','DEF','GHI') for cs in ('123','456','789')]

# TODO: Update the unit list to add the new diagonal units
left_diag_units = [[r+c for (r, c) in zip(rows, cols)]]

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jQuery(function($){
  var leftposition = $('.x-sidebar').position().left;
  $(window).scroll(function(){			
    if( $(this).scrollTop() >= ( $('.x-main').offset().top - $('.x-navbar-wrap').height() - 10 )  
    && ( $(this).scrollTop() < ( $('.x-main').height() + $('.x-main').offset().top - $('.widget_nav_menu').height() - $('.x-navbar-wrap').height() ) ) ) {
      $('.x-sidebar').css({position:'fixed', top:( $('.x-navbar-wrap').height() + 10 ), 
                           left: leftposition });
      }
    
    else if ( $(this).scrollTop() >= ( $('.x-main').height() + $('.x-main').offset().top - $('.widget_nav_menu').height() - $('.x-navbar-wrap').height() ) ) {

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precession_newt = function() {
  celestial_bodies = rbind(planets, dwarfplanets, asteroids)
  celestial_bodies = celestial_bodies[order(celestial_bodies$distance), ]
  celestial_bodies = celestial_bodies[complete.cases(celestial_bodies), ]
  
  precession = c(rep(0, nrow(celestial_bodies)))
  mass_sun = 1.989 * 10**30