danhammer

(ns forma.source.logistic
  (:use [clojure.math.numeric-tower :only (sqrt floor abs expt)]
        [clojure-csv.core])
  (:require [incanter.core :as i]))

(defn feature-vec
  [n]
  (map (partial cons 1)
       (for [x (range n)]
         (take 22 (repeatedly rand)))))

danhammer

def logistic_regression(x,
                        y, 
                        rdg_cons = 1.e-6,
                        verbose=False,
                        conv_thresh=1.e-8,
                        maxit=500):

    """Calculates the maximum likelihood estimates of a logistic
    regression.  Uses the [Newton-Raphson algorithm]
    (sites.stat.psu.edu/~jiali/course/stat597e/notes2/logit.pdf)

danhammer

(ns forma.source.logistic
  (:require [incanter.core :as i]))

(defn feature-vec
  [n]
  (map (partial cons 1)
       (for [x (range n)]
         (take 22 (repeatedly rand)))))

(defn dot-product

danhammer

import numpy as np

def test_my_mult(n):
    A = np.random.rand(n*23).reshape(n,23)
    At = A.T

    t0 = time.time()
    res = np.dot(A.T, A)
    print time.time() - t0
    print np.shape(res)

danhammer

(ns forma.trends.analysis
  (:use [forma.matrix.utils :only (variance-matrix coll-avg ones-column)]
        [clojure.math.numeric-tower :only (sqrt floor abs expt)]
        [forma.trends.filter :only (deseasonalize make-reliable hp-filter)]
        [clojure-csv.core])
  (:require [forma.utils :as utils]
            [incanter.core :as i]
            [incanter.stats :as s]))

;; TODO (dan): move thest extra matrix functions somewhere else

danhammer

import os, sys, json

import numpy as np

try:
    #import matplotlib.pylab as plt
    from scikits.timeseries.lib.interpolate import *
    import scikits.statsmodels.sandbox.stats.diagnostic as diag
    from scikits.statsmodels.tsa.filters import hpfilter
    import scikits.statsmodels.api as sm

danhammer

(ns forma.source.logistic
  (:use [forma.utils :only (dot-product transpose multiply-rows)]
        [clojure.contrib.math :only (abs)])
  (:require [incanter.core :as i])
  (:import [org.jblas FloatMatrix MatrixFunctions Solve DoubleMatrix]))

;; TODO: correct for error induced by ridge

(defn logistic-fn
  "returns the value of the logistic function, given input `x`"

danhammer

def convert_ts(time_series, start_year=2000, start_pd=4, freq=23):
     """
     Convert a numpy time-series into an rpy2 object, which, in turn,
     is a 'ts' object in R.  The 'ts' object is more wholly specified
     if a start date is provided.  For our applications at 16-day
     intervals for NDVI, this start date is April 2000, with a
     frequency of 23 observations each year.
     input:  numpy time-series
     output: rpy2 ts object

danhammer

(ns forma.classify.logistic
  (:use [forma.utils]
        [clojure.math.numeric-tower :only (abs)]
        [forma.matrix.utils]
        [cascalog.api])
  (:require [incanter.core :as i])
  (:import [org.jblas FloatMatrix MatrixFunctions Solve DoubleMatrix]))

;; TODO: correct for error induced by ridge

danhammer

(ns forma.hadoop.jobs.forma
  (:use cascalog.api)
  (:require [cascalog.ops :as c]
            [forma.trends.analysis :as a]
            [forma.classify.logistic :as log]))

(def get-loc
  (<- [?chunk :> ?s-res ?mod-h ?mod-v ?sample ?line ?val]
      (map ?chunk [:location :value] :> ?loc ?val)
      (schema/unpack-pixel-location ?loc :> ?s-res ?mod-h ?mod-v ?sample ?line)))