rayheberer
/ list_filtering.R
Created
September 29, 2020 21:38
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| builtin_data <- data()$results[, "Item"] | |
| builtin_df_names <- builtin_data %>% | |
| purrr::set_names() %>% | |
| purrr::map(purrr::possibly(get, otherwise = NULL)) %>% | |
| purrr::keep(is.data.frame) %>% | |
| purrr::map(names) %>% | |
| purrr::compact() |
rayheberer
/ cross.R
Created
September 29, 2020 21:37
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| colors <- c("red", "green", "yellow") | |
| vehicles <- c("bicycle", "car", "submarine", "airplane") | |
| colored_vehicles <- purrr::cross2(colors, vehicles) %>% | |
| purrr::map_chr(~paste(.[[1]], .[[2]])) |
rayheberer
/ nested.R
Created
September 29, 2020 21:37
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| colors <- c("red", "green", "yellow") | |
| vehicles <- c("bicycle", "car", "submarine", "airplane") | |
| colored_vehicles <- colors %>% | |
| purrr::map(function(color) { | |
| purrr::map_chr(vehicles, ~paste(color, .)) | |
| }) %>% | |
| purrr::flatten() %>% | |
| unlist() |
rayheberer
/ set_names.R
Created
September 29, 2020 21:36
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| animals <- c("lion", "tiger", "rhinoceros") | |
| animal_nchars <- animals %>% | |
| purrr::set_names() %>% | |
| purrr::map(nchar) |
rayheberer
/ locked_tomb_theory.txt
Created
August 26, 2020 20:24
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| Blood of Eden is made up of humans that left earth before the resurrection. Their "eden" is the earth itself, and they seek "revenge for the 10 billion" | |
| The 10 billion are the humans who were left on Earth when the resurrection - presumably following some extinction event - occurred. | |
| John orchestrated the extinction in order to ascend to godhood. By killing all life in the solar system, he created the 9 resurrection beasts. | |
| Alecto is the resurrection beast of Earth, which John made into his cavalier and performed perfect Lyctorhood with. | |
| So John is basically a super-lyctor powered by the soul of a planet, rather than of another human. |
rayheberer
/ make_barpolar.py
Created
July 29, 2020 18:39
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| import plotly.graph_objects as go | |
| import plotly.express as px | |
| def make_barpolar(vals, labels=None, colors=None, layout_options = None, **fig_kwargs): | |
| # infer slice angles | |
| num_slices = len(vals) | |
| theta = [(i + 1.5) * 360 / num_slices for i in range(num_slices)] | |
| width = [360 / num_slices for _ in range(num_slices)] | |
rayheberer
/ network.py
Created
May 31, 2020 17:28
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| import tensorflow as tf | |
| from tf.keras.regularizers import l2 | |
| def build_nn(input_shape, | |
| hidden_units=(20, 20), | |
| output_size=1, | |
| output_activation=None, | |
| hidden_activation=tf.nn.relu, | |
| dropout=0.0, | |
| regularization=0.0): |
rayheberer
/ network.py
Last active
May 31, 2020 17:25
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| import tensorflow as tf | |
| def build_nn(input_shape, hidden_units=(20, 20), output_size=1, output_activation=None): | |
| inputs = tf.keras.Input(shape=input_shape) | |
| hidden = inputs | |
| for layer_units in hidden_units: | |
| hidden = tf.keras.layers.Dense(layer_units, activation=tf.nn.relu)(hidden) | |
| outputs = tf.keras.layers.Dense(output_size, activation=output_activation)(hidden) |
rayheberer
/ plotting-3d-functions
Created
March 1, 2020 20:18
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| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from mpl_toolkits import mplot3d | |
| def plot_surface(domain, fn, grid_samples=100, title=None, **plot_kwargs): | |
| x = np.linspace(domain[0][0], domain[0][1], grid_samples) | |
| y = np.linspace(domain[1][0], domain[1][1], grid_samples) | |
| X, Y = np.meshgrid(x, y) | |
rayheberer
/ plot-sample-landscape.py
Created
February 3, 2020 02:06
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| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from mpl_toolkits import mplot3d | |
| def plot_surface(domain, fn, title=None, grid_samples=100): | |
| x = np.linspace(domain[0][0], domain[0][1], grid_samples) | |
| y = np.linspace(domain[1][0], domain[1][1], grid_samples) | |
| X, Y = np.meshgrid(x, y) | |
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