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author | Gengxin Xie <gengxin.xie@intel.com> | 2020-03-16 14:51:15 +0800 |
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committer | Gengxin Xie <gengxin.xie@intel.com> | 2020-03-16 16:44:31 +0800 |
commit | a5cfc8eb36e149d8da591003fdfec04a955c9565 (patch) | |
tree | 8ccad1694fdcbcd7b4b6ff6bc5330bab9913b77b | |
parent | b1cb23b2f0f6da256c1473ab303216ce653f882c (diff) | |
download | numpy-a5cfc8eb36e149d8da591003fdfec04a955c9565.tar.gz |
MAINT: provide float64 logisticregression bench
-rw-r--r-- | benchmarks/benchmarks/bench_avx.py | 14 |
1 files changed, 8 insertions, 6 deletions
diff --git a/benchmarks/benchmarks/bench_avx.py b/benchmarks/benchmarks/bench_avx.py index 224c12e33..2a128b3ff 100644 --- a/benchmarks/benchmarks/bench_avx.py +++ b/benchmarks/benchmarks/bench_avx.py @@ -131,6 +131,8 @@ class Mandelbrot(Benchmark): self.mandelbrot_set(-0.74877,-0.74872,0.06505,0.06510,1000,1000,2048) class LogisticRegression(Benchmark): + param_names = ['dtype'] + params = [np.float32, np.float64] timeout = 1000 def train(self, max_epoch): @@ -142,16 +144,16 @@ class LogisticRegression(Benchmark): dw = (1/self.size) * np.matmul(self.X_train.T, dz) self.W = self.W - self.alpha*dw - def setup(self): + def setup(self, dtype): np.random.seed(42) self.size = 250 features = 16 - self.X_train = np.float32(np.random.rand(self.size,features)) - self.Y_train = np.float32(np.random.choice(2,self.size)) + self.X_train = np.random.rand(self.size,features).astype(dtype) + self.Y_train = np.random.choice(2,self.size).astype(dtype) # Initialize weights - self.W = np.zeros((features,1), dtype=np.float32) - self.b = np.zeros((1,1), dtype=np.float32) + self.W = np.zeros((features,1), dtype=dtype) + self.b = np.zeros((1,1), dtype=dtype) self.alpha = 0.1 - def time_train(self): + def time_train(self, dtype): self.train(1000) |