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# coding: utf-8 | |
"""Online learning.""" | |
import numpy as np | |
from numpy import sign | |
import itertools as it | |
from numpy import array as A, zeros as Z | |
import math |
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""" | |
Code for training RBMs with contrastive divergence. Tries to be as | |
quick and memory-efficient as possible while utilizing only pure Python | |
and NumPy. | |
""" | |
# Copyright (c) 2009, David Warde-Farley | |
# All rights reserved. | |
# | |
# Redistribution and use in source and binary forms, with or without |
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class OnlineLearner(object): | |
def __init__(self, **kwargs): | |
self.last_misses = 0. | |
self.iratio = 0. | |
self.it = 1. | |
self.l = kwargs["l"] | |
self.max_ratio = -np.inf | |
self.threshold = 500. | |
def hinge_loss(self, vector, cls, weight): |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
from __future__ import with_statement | |
import collections, operator, math, random, pprint | |
class Classifier(object): | |
AttrsToDump = ["value_counts", "class_counts", "features", "feature_counts"] | |
def __init__(self, features={}, verbose=False): |
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#!/usr/bin/env python | |
# encoding: utf-8 | |
""" | |
client.py | |
Based heavily on: | |
- https://github.com/mtah/python-websocket/blob/master/examples/echo.py | |
- http://stackoverflow.com/a/7586302/316044 | |
Created by Drew Harry on 2011-11-28. |
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from pylab import * | |
from scipy.ndimage import measurements | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import time | |
import sys | |
import signal | |
def signal_handler(signal, frame): | |
sys.exit(0) |
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create temporary sequence predictor_seq; | |
CREATE temporary TABLE linear_systems_test_data( id INTEGER NOT NULL, lhs DOUBLE PRECISION[], rhs DOUBLE PRECISION); | |
insert into linear_systems_test_data values (nextval('predictor_seq'), '{352137.0,84.7067061967771,-150.185137392799,122.050417659229,-74.0884874537119,99.7911494563722,-3.7907455558484,181224.690513009,106938.029173016,69336.1362009346,18335.0,12.7007088793537,-250.440511627437,-62.5561474971013,-292.703374755409,-167.012940969203,-78.511449979818,9451.24681999999,5585.85191537643,3628.50938401}'::float[], 88920344.0 ); | |
insert into linear_systems_test_data values (nextval('predictor_seq'), '{84.7067061967771,176105.544243727,61.0252088296143,-73.1971959184637,7.54222162981446,-48.6063990056131,-37.7170700467655,-11006.0678697046,-11871.4874441653,-10787.6157862104,12.7007088793537,9313.85168737771,-31.2780737485506,-85.9645308238089,-89.8568249242787,-78.6462830692247,10.8622991388217,-566.613529886409,-617.49619500345,-565.289335368564}'::float[] |
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// http://is.gd/gWewbP | |
extern mod extra; | |
use extra::treemap::TreeMap; | |
use extra::json::ToJson; | |
use extra::serialize::Decodable; | |
use json = extra::json; | |
// structs are required for decoding a JSON object into a Rust object | |
#[deriving(Decodable)] |
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import numpy as np | |
class RingBuffer(np.ndarray): | |
'A multidimensional ring buffer.' | |
def __new__(cls, input_array): | |
obj = np.asarray(input_array).view(cls) | |
return obj |
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