def kmeans(X, assign, k):
C = par.map(calc_centroid, arange(k), fixed=(X, assign))
converged = False
while not converged:
lastAssign = assign
assign = par.map(minidx, X, fixed=C)
converged = np.all(assign == lastAssign)
C = parakeet.map(calc_centroid, arange(k), fixed=(X, assign))
return C
def test_black_scholes(): CallFlags = np.array([True, False, True, False]) A = np.array([2.0, 1.0, 2.0, 1.0]) inputs = (CallFlags, A, A, A, A, A) x = parakeet.map(scalar_black_scholes, *inputs) y = slow_black_scholes(*inputs) print "BLACK SCHOLES RESULTS Parakeet = %s\n Python = %s" % (x,y) assert np.all(np.abs(x - y) < 0.00001)
def test_cnd(): print "RUNNING CND" x = np.arange(10000, dtype='float32') y = parakeet.map(CND, x) z = np.array([CND(xi) for xi in x]) print "Parakeet: ", y print "Python: ", z assert np.sum(abs(y-z)) < 0.00001
def test_rule30():
output = init.copy()
cur = init
zero_array = np.array([0])
indices = np.arange(1,size+1)
for _ in range(size/2):
extended = np.concatenate((zero_array, cur, zero_array))
def run_rule30(i):
return rule30(extended, i)
parakeet_iter = parakeet.map(run_rule30, indices)
cur = np.array(map(run_rule30, indices))
assert eq(parakeet_iter, cur), \
"Parakeet result (%s) didn't match Python result(%s)" % \
(parakeet_iter, cur)
output = np.vstack((output,cur))
def norm_cdf(ary):
def _inner(x):
a1 = 0.31938153
a2 = -0.356563782
a3 = 1.781477937
a4 = -1.821255978
a5 = 1.330274429
L = abs(x)
K = 1.0 / (1.0 + 0.2316419 * L)
w = 1.0 - 1.0/sqrt(2*3.141592653589793) * exp(-1*L*L/2.) * (a1*K +
a2*K*K + a3*K*K*K + a4*K*K*K*K + a5*K*K*K*K*K)
if x < 0:
w = 1.0-w
return w
return parakeet.map(_inner, ary)
def test_rule30():
output = init.copy()
cur = init
zero_array = np.array([0])
indices = np.arange(1, size + 1)
for _ in range(size / 2):
extended = np.concatenate((zero_array, cur, zero_array))
def run_rule30(i):
return rule30(extended, i)
parakeet_iter = parakeet.map(run_rule30, indices)
cur = np.array(map(run_rule30, indices))
assert eq(parakeet_iter, cur), \
"Parakeet result (%s) didn't match Python result(%s)" % \
(parakeet_iter, cur)
output = np.vstack((output, cur))
def norm_cdf(ary):
def _inner(x):
a1 = 0.31938153
a2 = -0.356563782
a3 = 1.781477937
a4 = -1.821255978
a5 = 1.330274429
L = abs(x)
K = 1.0 / (1.0 + 0.2316419 * L)
w = 1.0 - 1.0 / sqrt(2 * 3.141592653589793) * exp(
-1 * L * L / 2.) * (a1 * K + a2 * K * K + a3 * K * K * K +
a4 * K * K * K * K + a5 * K * K * K * K * K)
if x < 0:
w = 1.0 - w
return w
return parakeet.map(_inner, ary)
def mult_vec_scalar(x,y): return parakeet.map(parakeet.mult, x, y)
def dct(I): return par.map(dct_val, idxs, fixed=[I])
def map_add(x): return parakeet.map(parakeet.add, x, x)
def map_increase_rank(x): return parakeet.map(increase_rank, x)
def simple_map(x): return parakeet.map(incr, x)
def calc_assigns(X, C): return parakeet.map(calc_assign, X, fixed=[C])
def calc_centroids(X, assign, a): return parakeet.map(calc_centroid, a, fixed=[X, assign])
def calc_assign(C, x): return np.argmin(parakeet.map(sqr_dist, C, fixed=[x]))
def map_mult_cols(mat, col): return parakeet.map(parakeet.mult, mat, col, axis=0)
def simple_map(x): return parakeet.map(incr, x)
def minidx(C, x):
return par.argmin(parakeet.map(sqr_dist, C, fixed=x))
def map_increase_rank(x): return parakeet.map(increase_rank, x)
