jcrudy

from scipy.stats.kde import gaussian_kde
import pymc
from math import log
from matplotlib import pyplot

def KernelSmoothing(name, dataset, bw_method=None, lower=float('-inf'), upper=float('inf'), observed=False, value=None):
    '''Create a pymc node whose distribution comes from a kernel smoothing density estimate.'''
    density = gaussian_kde(dataset, bw_method)
    lower_tail = 0
    upper_tail = 0

jcrudy

'''
=============================
Comparison with the R package
=============================


This script randomly generates earth-style models, then randomly generates data from those models and
fits earth models to those data using both the python (:class:`Earth`) and R implementations.  It records the sample size,
m, the number of input dimensions, n, the number of forward pass iterations, the runtime, and the r^2
statistic for each fit and writes the result to a CSV file.  This script requires pandas, rpy2, and a

jcrudy

from sklearn.ensemble import AdaBoostRegressor
from sklearn.metrics import r2_score
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from pyearth import Earth
import numpy as np
import pandas as pd

# Generate a data set
np.random.seed(1)

jcrudy

"""
The :mod:`sklearn.pipeline` module implements utilites to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
#         Gael Varoquaux
#         Virgile Fritsch
#         Alexandre Gramfort
# Licence: BSD

jcrudy

from pyearth import Earth
from sklearn.pipeline import Pipeline
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn import datasets
import numpy as np
np.random.seed(1)

# Get data
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)

jcrudy

import scala.util.continuations._

class Generator[A] extends Iterator[A] with (A => Unit @ suspendable) {
    private var a: Option[A] = None
    private var k: Option[Unit => Unit] = None
    
    def next = {
	val a0 = a.get
	val k0 = k.get
        a = None

jcrudy

# Produce some simulated survival data from a weird hazard function
import numpy
from samplers import HazardSampler

# Set a random seed and sample size
numpy.random.seed(1)
m = 1000

# Use this totally crazy hazard function
hazard = lambda t: numpy.exp(numpy.sin(t) - 2.0)

jcrudy

from sqlalchemy.ext.automap import automap_base
from sqlalchemy import create_engine, MetaData, Column, String, Integer
import pickle
from sqlalchemy.sql.schema import ForeignKey

# Create some tables in the database
engine = create_engine('sqlite://')
engine.execute('CREATE TABLE user (id INTEGER, name TEXT, favorite_color TEXT)')
engine.execute('CREATE TABLE profile (id INTEGER, userid INTEGER, summary TEXT)')

jcrudy

from sympy import Symbol, Add, Mul, Max, RealNumber
from sympy.utilities.codegen import codegen

vars = {'x0': Symbol('x0'),
        'x1': Symbol('x1')}

coefficients = [.5, 1.4, 9.8]

terms = [RealNumber(1), Max(0, Add(vars['x0'], RealNumber(-3.5))), 
         Mul(Max(RealNumber(0), Add(vars['x0'], RealNumber(-3.5))), Max(RealNumber(0), Add(vars['x1'], RealNumber(-6))))]