fix qloguniform issue

docs/en_US/Tutorial/SearchSpaceSpec.md

@@ -45,7 +45,7 @@ All types of sampling strategies and their parameter are listed here:
   * When optimizing, this variable is constrained to a two-sided interval.
 
 * {"_type":"quniform","_value":[low, high, q]}
-  * Which means the variable value is a value like randint(np.floor((high-low)/q)+1)*q + low. For example, for _value specified as [0, 10, 2.5], possible values are [0, 2.5, 5.0, 7.5, 10.0]; For _value specified as [2, 10, 5], possible values are [2, 7]. All possible values have the same possibility to be selected.   
+  * Which means the variable value is a value like randint(floor((high-low)/q)+1)*q + low. For example, for _value specified as [0, 10, 2.5], possible values are [0, 2.5, 5.0, 7.5, 10.0]; For _value specified as [2, 10, 5], possible values are [2, 7]. All possible values have the same possibility to be selected.   
 
   * Suitable for a discrete value with respect to which the objective is still somewhat "smooth", but which should be bounded both above and below. If you want to uniformly choose integer from a range [low, high], you can write `_value` like this: `[low, high, 1]`.
 
@@ -54,7 +54,7 @@ All types of sampling strategies and their parameter are listed here:
   * When optimizing, this variable is constrained to be positive.
 
 * {"_type":"qloguniform","_value":[low, high, q]}
-  * Which means the variable value is a value like round(loguniform(low, high)) / q) * q
+  * Which means the variable value is a value like floor((loguniform(low, high) - low) / q) * q + low
   * Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.
 
 * {"_type":"normal","_value":[mu, sigma]}

src/sdk/pynni/nni/parameter_expressions.py

@@ -77,7 +77,7 @@ def qloguniform(low, high, q, random_state):
     q: sample step
     random_state: an object of numpy.random.RandomState
     '''
-    return np.round(loguniform(low, high, random_state) / q) * q
+    return np.floor((loguniform(low, high, random_state) - low) / q) * q + low
 
 
 def normal(mu, sigma, random_state):