craffel

import matplotlib.pyplot as plt

def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])
    

craffel

"""
Functions for computing the population count (aka Hamming weight) of an array
in-place using the built-in popcount routine.
Works with any integer datatype 64 bit-width or smaller.
Compile with gcc flag -mpopcnt
Adapted from
https://gist.github.com/aldro61/f604a3fa79b3dec5436a by Alexandre Drouin
"""
import numpy as np
cimport numpy as np

craffel

craffel

def mean_reciprocal_rank(X, Y, indices, metric='hamming'):
    ''' Computes the mean reciprocal rank of the correct match
    Assumes that X[n] should be closest to Y[n]
    Default uses hamming distance
    :parameters:
        - X : np.ndarray, shape=(n_examples, n_features)
            Data matrix in X modality
        - Y : np.ndarray, shape=(n_examples, n_features)
            Data matrix in Y modality
        - indices : np.ndarray

craffel

craffel

import tempfile
import subprocess
import os
import pretty_midi
import librosa

def fast_fluidsynth(m, fs):
    '''
    Faster fluidsynth synthesis using the command-line program
    instead of pyfluidsynth.

craffel

:%s/,\([^ ]\)/, \1/g

craffel

import os
os.chdir('..')
import sys
sys.path.append(os.getcwd())
import normalize_names
import pickle

with open('data/Clean MIDIs-md5_to_artist_title.pickle') as f:
    md5_to_artist_title = pickle.load(f)

craffel

craffel

"""
Create a tab-separated value file for uspop2002 entries which includes each
track's Echo Nest "loudness" score
"""

import pyen
import os
import time

# Put your Echo Nest API key in a file called .echonest_key